Projeto_Álbum_de_Figurinhas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projeto Álbum de Figurinhas.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPHVKiS55s6mV+CnkbEIngu",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Python-para-Machine-Learning/blob/master/Projeto_%C3%81lbum_de_Figurinhas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0bCJjZ8YZVb"
      },
      "source": [
        "![texto alternativo](https://github.com/gitflai/imagens/blob/main/capa.jpg?raw=true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgKi_rebQYm9"
      },
      "source": [
        "\n",
        "Essa é uma introdução bem rápida aos conceitos fundamentais de programação em Python. Se familiarizar com tais conceitos é um primeiro passo suficiente para fazer seus primeiros programas e também entender outros códigos feitos em Python. No entanto, não se engane, o universe de Python é gigante e existe muita coisa interessante para se conhecer ainda!\n",
        "\n",
        "Após terminar essa introdução, não deixe de se desafiar nos exercícios propostos! São importantíssimos para criar manejo e intimidade com a linguagem. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoQw4n_SMn9P"
      },
      "source": [
        "## **Conteúdo desse notebook:**\n",
        "\n",
        "1. [Atribuição de Variáveis](#atri)\n",
        "2. [Primeiras Funções](#prim)\n",
        "3. [Operadores Aritméticos](#oper)\n",
        "4. [Comparações e Booleanos](#comp)\n",
        "5. [Condicional IF-ELSE](#cond)\n",
        "6. [Definição de Funções](#defi) \n",
        "6. [Importação de Bibliotecas](#impo) \n",
        "6. [A Biblioteca Numpy](#nump)  \n",
        "6. [Listas](#lists) \n",
        "6. [Métodos de Listas](#metl) Iteradores FOR e WHILE \n",
        "6. [Funções Importantes](#func) \n",
        "6. [Iteradores FOR e WHILE](#iter) \n",
        "6. [Projeto: Álbum de Figurinhas](#proj) \n",
        "\n",
        "7. [Exercícios](#exer) \n",
        "9. [Links Úteis](#links)\n",
        "10. [Anexo: Zen of Python](#anex)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kMiWTL0pvFF"
      },
      "source": [
        "## **Atribuição de Variáveis** <a name=\"atri\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwHX8egGBuTc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "ed01ed77-adb5-4142-88ac-dcb29a53e006"
      },
      "source": [
        "meu_nome = 'Ricardo'\n",
        "meu_sobrenome = 'Rocha'\n",
        "meu_produto = \"Caixa d'agua\"\n",
        "minha_frase = '\"Vamos que vamos!\"'\n",
        "\n",
        "print(meu_nome)\n",
        "print(meu_sobrenome)\n",
        "print(meu_produto)\n",
        "print(minha_frase)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ricardo\n",
            "Rocha\n",
            "Caixa d'agua\n",
            "\"Vamos que vamos!\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_tw5S1Hq0Ep"
      },
      "source": [
        "Não é possível atribuir variáveis com nomes começando com números, nem com espaços no nome ou com aspas. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWqVaqeBrAb_"
      },
      "source": [
        "OBS.: O **hashtag #** na frente de uma linha torna ela um **comentário**, pois o interpretador ignora tudo que vem depois dela "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38emYI_xJqeO"
      },
      "source": [
        "## **Primeiras Funções** <a name=\"prim\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JyT6165pi6l"
      },
      "source": [
        "Conheça a função **len**, que retorna o tamanho de uma string (e de outros tipos de objetos também).\n",
        "\n",
        "A função **type**, que retorna o tipo do objeto na variável de entrada\n",
        "\n",
        "A função **round**, que arredonda um número de acordo com as casas decimas desejadas\n",
        "\n",
        "E a função **help**, que retorna a documentação da função inserida como entrada."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNTROt8rb4Lg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7aedb501-b5ac-4956-ea3d-eb7879efe1d2"
      },
      "source": [
        "nome = 'Ricardo'\n",
        "len(nome)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZH4vZ7NtI5Jx"
      },
      "source": [
        "Não só no Python, mas em programação em geral, sempre precisamos controlar o tipo das variáveis que utilizamos. Veja o exemplo abaixo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvKNUkrPIlDt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "793aaf4a-23d5-43af-f82a-c37ea1050044"
      },
      "source": [
        "minha_idade = '33'\n",
        "type(minha_idade)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyCVd6Tov_ao"
      },
      "source": [
        "Usando a função **int** para converter a variável em formato string para o formato inteiro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svsiydRLv8vT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8acee7fd-17cd-49d3-df49-23ef86642b69"
      },
      "source": [
        "\n",
        "minha_idade = int(minha_idade)\n",
        "type(minha_idade)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qXsK2dtD8QU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUH_pzLDJa4Q"
      },
      "source": [
        "## **Operadores Aritméticos** <a name=\"oper\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bS7hxv-Codpz"
      },
      "source": [
        "Confira na tabela abaixo como funcionam os operadores aritméticos em Python. \n",
        "\n",
        "Em especial, veja que a sintaxe da exponenciação não é feita através do sinal '^'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHsbgk0UGIaL"
      },
      "source": [
        "| Operator     | Name           | Description                                            |\n",
        "|--------------|----------------|--------------------------------------------------------|\n",
        "| ``a + b``    | Adiçao       | Soma entre ``a`` e ``b``                                 |\n",
        "| ``a - b``    | Subtração    | Diferença entre ``a`` e ``b``                          |\n",
        "| ``a * b``    | Multiplicação | Produto entre ``a`` e ``b``                             |\n",
        "| ``a / b``    | Divisão  | Divisão usual entre ``a`` e ``b``                            |\n",
        "| ``a // b``   | Divisão inteira | A divisão entre ``a`` e ``b``, removendo as partes decimais |\n",
        "| ``a % b``    | Resto da divisão        | O resto da divisão inteira de ``a`` por ``b``     |\n",
        "| ``a ** b``   | Exponenciação | ``a`` elevado a ``b``                     |\n",
        "| ``-a``       | Negação       | O negativo de ``a``                                  |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9gTYGKo31B4"
      },
      "source": [
        "print(a + b)\n",
        "print(a - b)\n",
        "print(a * b)\n",
        "print(a / b)\n",
        "print(a // b)\n",
        "print(a % b)\n",
        "print(a ** b)\n",
        "print(-a)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLOeFbsZGcx2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "35dabf11-ce8f-4f7a-c352-aedbddcaa52b"
      },
      "source": [
        "print(max(a, b))\n",
        "print(min(a, b))\n",
        "print(abs(-a))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "1\n",
            "-2\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lh6dGL01YdHZ"
      },
      "source": [
        "## **Comparações e Booleanos** <a name=\"comp\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJGYOSKzn62F"
      },
      "source": [
        "Quando comparamos objetos em Python é como se estivéssemos fazendo uma pergunta a ele. Duas variáveis são iguais? Uma é maior que a outra? \n",
        "\n",
        "E a resposta é um objeto do tipo **booleano**, que é indicado por **True** ou **False**, indicando, respectivamente, se a resposta é verdadeira ou falsa."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h13Wb-T0YoL8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8d504728-9ffe-44bc-9943-63d7c18df4fa"
      },
      "source": [
        "var1 = 35\n",
        "var2 = 36\n",
        "\n",
        "var1 == var2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_tNf7CldSvp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "07c6be9d-fc00-4a02-c9c4-12a68097b444"
      },
      "source": [
        "print(type(33.2) == str)\n",
        "print(type(33.2) == int)\n",
        "print(type(33.2) == float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n",
            "False\n",
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BwQWdHmQCuY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "119bbfcf-fb20-4d06-802a-ec1dc6e6aba6"
      },
      "source": [
        "True and True\n",
        "True or False\n",
        "not False"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SF6EtbmrZQO3"
      },
      "source": [
        "## **Condicional IF-ELSE** <a name=\"cond\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9WtbCPDAWkW"
      },
      "source": [
        "Veja abaixo como funciona a estrutura geral de condicionais no Python. \n",
        "\n",
        "Temos a versão IF ELSE, e a versão IF ELIF ELSE.\n",
        "\n",
        "Note que é possível utilizar o ELIF várias vezes se necessário.\n",
        "\n",
        "No espaço de condição, o Python espera um objeto booleano. Se for True, ele executa, se for False, segue adiante. \n",
        "\n",
        "Note também a estrutura de identação do Python. Aqui não usamos (), [] ou {} para estipular o que deve ser executado nas condicionais. Simplesmente colocamos o código que deve ser executado na linha seguinte, mas identado. Em geral, se utiliza o espaço de **1 tab**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuqS_I9GdobX"
      },
      "source": [
        "# sintaxe IF-ELSE geral\n",
        "if condição:\n",
        "    executa aqui\n",
        "else:\n",
        "    executa aqui\n",
        "\n",
        "# sintaxe IF-ELIF-ELSE geral\n",
        "if condição:\n",
        "    executa aqui\n",
        "elif outra_condição:\n",
        "    executa aqui\n",
        "else:\n",
        "    executa aqui\n",
        "\n",
        "#não executar essa célula!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7Fw4opvdznZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "83a5d3fb-5898-4627-de2c-56f8f85f723d"
      },
      "source": [
        "a, b, c = 1, -2 , 0\n",
        "\n",
        "delta = b**2 - 4*a*c\n",
        "\n",
        "if delta > 0:\n",
        "    x1 = (-b + delta**0.5)/(2*a)\n",
        "    x2 = (-b - delta**0.5)/(2*a)\n",
        "    print('As raízes são {} e {}'.format(x1, x2))\n",
        "elif delta == 0:\n",
        "    x = -b/(2*a)\n",
        "    print('A única raíz é {}'.format(x))\n",
        "else:\n",
        "    print('Não há soluções reais para essa equação')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "As raízes são 3.0 e 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3K7wNqEIembT"
      },
      "source": [
        "## **Definição de Funções** <a name=\"defi\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEMYVdTLlf5t"
      },
      "source": [
        "Uma função é um recurso para não precisarmos repetir o mesmo código desnecessariamente. Assim como as funções da matemática, sua tarefa é transformar um input, uma entrada, uma coleção de variáveis, em uma saída, um output. \n",
        "\n",
        "No exemplo abaixo, utilizamos como variáveis o tipo de símbolo e o tamanho da linha que queremos fazer. Você entenderá melhor vendo o exemplo:\n",
        "\n",
        "Da mesma maneira que na estrutura de condicionais, o conteúdo de uma função fica nas linhas seguintes sendo indicados pela **indentação** do código"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQ-INeRi6tTG"
      },
      "source": [
        "def Mensagem(simbolo = '*', tamanho = 51):\n",
        "    print(simbolo*tamanho)\n",
        "    print('Estamos só começando, o melhor ainda estar por vir!')\n",
        "    print(simbolo*tamanho)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLiKW1mVu3B2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "a5f2236f-48c0-4e88-d196-c2d8df28772d"
      },
      "source": [
        "Mensagem('-', 51)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------\n",
            "Estamos só começando, o melhor ainda estar por vir!\n",
            "---------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8XyjoLsmZB3"
      },
      "source": [
        "Voltando no exemplo da fórmula de Bhaskara, fica bem conveniente quando colocamos no formato de função, para analisar as saídas de maneira mais prática. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9s_3C-ZrgKPb"
      },
      "source": [
        "def Bhaskara(a, b, c):\n",
        "    # dando uma resposta adequada para quando o valor de a for igual a zero\n",
        "    if a == 0:\n",
        "        print('Quando a = 0 não se aplica Bhaskara')\n",
        "    else:\n",
        "        delta = b**2 - 4*a*c\n",
        "\n",
        "        if delta > 0:\n",
        "            x1 = (-b + delta**0.5)/(2*a)\n",
        "            x2 = (-b - delta**0.5)/(2*a)\n",
        "            print('As raízes são {} e {}'.format(round(x1, 2), round(x2, 2)))\n",
        "        elif delta == 0:\n",
        "            x = -b/(2*a)\n",
        "            print('A única raíz é {}'.format(x))\n",
        "        else:\n",
        "            print('Não há soluções reais para essa equação')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zs9zyfICgddJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "17e12e19-8632-4ca0-a6ea-e18897d1f8ef"
      },
      "source": [
        "Bhaskara(3, 2, 0.9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Não há soluções reais para essa equação\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-eQY_VJ9voY"
      },
      "source": [
        "## **Listas** <a name=\"lists\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgBgFBTo-8gx"
      },
      "source": [
        "Um objeto do tipo lista é um dos mais importantes do universo Python. Sua versatilidade permite a construção e organização simples de diversas funcionalidades. **Uma lista é definida por conchetes [ ]**. Vejamos alguns exemplos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxQQgv2j_TGn"
      },
      "source": [
        "minha_lista = [1, 1, 2, 3, 5, 8, 13]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxt_3Q_h_JuO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c89f09ba-e51d-4f3a-9cfc-ed8bb460a313"
      },
      "source": [
        "print(minha_lista)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 1, 2, 3, 5, 8, 13]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twfQLsSOBFUi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e50d25e7-ea5e-47e8-e55c-8f5ba83e90ff"
      },
      "source": [
        "len(minha_lista)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2s66TOcfAhh5"
      },
      "source": [
        "**Importante**: A indexação no Python, assim como em diversas linguagens de programação, começam com índice 0\n",
        "\n",
        "Isso quer dizer que o primeiro elemento está na entrada 0 da lista, o segundo elemento na entrada 1, e assim por diante.\n",
        "\n",
        "Nós acessamos os elementos de uma lista também utilizando **colchetes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFrs9gJt_hoo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "58bf7836-e73a-4246-cb5b-a943a10b1164"
      },
      "source": [
        "minha_lista[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLz1CsHF_rQx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c79c68fc-fd1b-40b9-beba-ff47b276b9f4"
      },
      "source": [
        "minha_lista[len(minha_lista) - 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCxiEy34_95J"
      },
      "source": [
        "Uma lista permite armazenar todos os tipos de objetos do Python, veja no exemplo abaixo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPMB_H5-BSHQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f7da50e4-8846-4ab9-a3df-20c73313b983"
      },
      "source": [
        "lista = ['Python', 1996, True, len, [1,2,'Ricardo']]\n",
        "print(lista)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Python', 1996, True, <built-in function len>, [1, 2, 'Ricardo']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ub4FXtitBZW4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "623eb9e9-d81d-4ae2-8ea3-3869cac9aae9"
      },
      "source": [
        "lista[0] + ' é muito massa' "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Python é muito massa'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vQfJXe_BckY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "97d9e804-836a-4ba3-e00c-b24b99edbd22"
      },
      "source": [
        "lista[1] + 1337"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_p8LnFNoBy_Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c6f681c0-d2c6-4e56-bd37-92e70234d1a9"
      },
      "source": [
        "lista[2] and True"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYbglI3QB49K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ce4b9179-6cb4-4fbb-f7c2-8c82656c67bb"
      },
      "source": [
        "lista[3](lista)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYaycgzoB8RQ"
      },
      "source": [
        "Podemos acessar caracteres de listas através de colchetes também. No python, strings são como listas de caracteres:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tc7JYUWuCHbo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "79f88f58-8338-4873-cbed-99209f1e585d"
      },
      "source": [
        "'Python'[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'P'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_P_pkjYZCHfG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b57638e3-2f40-48a1-dd87-a16579e31ca9"
      },
      "source": [
        "py = 'Python'\n",
        "py[0] + py[5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pn'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DK3LkMgqCOrR"
      },
      "source": [
        "E podemos acessar listas dentro de listas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ce3loc-iCwp_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6dd17226-c3c2-4962-ca3f-f2f83ccddd29"
      },
      "source": [
        "lista[4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01UJ8UcBC2bB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "71193235-ba48-450d-bc07-a51a3377455d"
      },
      "source": [
        "lista[4][2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Ricardo'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFacnDGgDApB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9c56f0a4-22d6-4cd9-959e-f43961306413"
      },
      "source": [
        "lista[4][2][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'R'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8PlzPF1Cx-w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "96036126-5eee-48aa-99fb-07cad14b0d69"
      },
      "source": [
        "lista[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Python'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jAN0-DFC42g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c992f6df-4438-4cb2-b046-0ca0276ed930"
      },
      "source": [
        "lista[0][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'P'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMa7ivrxC7EL"
      },
      "source": [
        "Alguns comandos úteis para acessar elementos em listas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7EWm9MaDd7R"
      },
      "source": [
        "frase = 'Python é excelente para análise de dados'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GFA9PY1Dloy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e4ec071c-d743-4b13-e47c-0587dfd8acf8"
      },
      "source": [
        "frase[:6]  #retorna todos os elementos até o indice 6 da lista (o indice 6 (sétimo elemento) não está incluido)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Python'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5aR7aeGD1gT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cd62322d-8733-4cfd-aa28-78a6fb41ccf1"
      },
      "source": [
        "frase[6:]  #retorna todos os elementos após o indice 6 da lista"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' é excelente para análise de dados'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpzQ0wxyESUY"
      },
      "source": [
        "No Python, o padrão é trabalhar com **intervalos semi-abertos [ , )**, como no exemplo abaixo. O índice 9 está incluso na seleção, mas não o 18. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8g1SouLeD5hJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b54e34ec-95da-4bb5-f53a-183614f26f45"
      },
      "source": [
        "frase[9:18] # retorna o elemento de indice 9 até o 18, mas não inclui o 18"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'excelente'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PPDP_x3Elvp"
      },
      "source": [
        "Para inverter a ordem de uma lista, utilize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6Q_Pg2iEl1B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b534e11-ab52-4d4d-f564-3febd93ba90e"
      },
      "source": [
        "frase[::-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sodad ed esilána arap etnelecxe é nohtyP'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRWDbZLXFBy4"
      },
      "source": [
        "Para tomar elementos de 2 em 2, utilize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4VPNkkEFHSY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2658caa8-a85b-4940-d364-4cb6800e6e30"
      },
      "source": [
        "frase[::2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pto  xeet aaaáied ao'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgfaeacMElyi"
      },
      "source": [
        "Combine os comandos da maneira que lhe for conveniente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXNjLt4AEiyo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "be78d32b-f44f-4811-d27b-72a0872fe5e9"
      },
      "source": [
        "frase[::-1][::3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'saeelar nee hP'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAMGBVITD6_M"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vmnCWv9mNwX"
      },
      "source": [
        "## **Importação de Bibliotecas** <a name=\"impo\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuvRAAl0mTD8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c737cdf9-c6c8-4ece-c97b-37564642cd32"
      },
      "source": [
        "from math import pi\n",
        "print(pi)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.141592653589793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoE-Zyp5ma7S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d274206f-9a38-4e95-96ed-7361ad8ba436"
      },
      "source": [
        "import math\n",
        "print(math.pi)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.141592653589793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCtUvjfY6Nbw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0522ab9f-37f4-4902-85fd-b6a17bc4976f"
      },
      "source": [
        "import math as m\n",
        "print(m.pi)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.141592653589793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4qDl7aWXlnN"
      },
      "source": [
        "## **A Biblioteca Numpy** <a name=\"nump\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBb8VajVYDEb"
      },
      "source": [
        "Numpy é a biblioteca mais popular do python para se trabalhar com arrays. \n",
        "\n",
        "Arrays são conjuntos de valores, que podem ser n-dimensionais. \n",
        "\n",
        "Geralmente, um número apenas, um ponto, é um array de dimensão 0\n",
        "\n",
        "Uma lista de valores é um array de dimensão 1. \n",
        "\n",
        "Uma matriz de valores é uma array de dimensão 2. \n",
        "\n",
        "Um tensor de valores é um array de dimensão 3. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvdq1LlPZXKH"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64NE6_M6ZYxh"
      },
      "source": [
        "a0 = np.array(1)\n",
        "a1 = np.array([1, 2])\n",
        "a2 = np.array([[1, 2], [3, 4]])\n",
        "a3 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YWoD5e6aVwu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "93511906-d017-4028-8d90-196e784b2fbd"
      },
      "source": [
        "print('Array a0:\\n', a0)\n",
        "print('Array a1:\\n', a1)\n",
        "print('Array a2:\\n', a2)\n",
        "print('Array a3:\\n', a3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Array a0:\n",
            " 1\n",
            "Array a1:\n",
            " [1 2]\n",
            "Array a2:\n",
            " [[1 2]\n",
            " [3 4]]\n",
            "Array a3:\n",
            " [[[1 2]\n",
            "  [3 4]]\n",
            "\n",
            " [[5 6]\n",
            "  [7 8]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYn8Dx86Zcpw"
      },
      "source": [
        "Use o atributo ndim para verificar a dimensão dos arrays numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGQQ1uQBZaLe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "0231212e-1bed-4d56-be04-bb8a8a89ff44"
      },
      "source": [
        "print('Dimensão de a0:', a0.ndim)\n",
        "print('Dimensão de a1:', a1.ndim)\n",
        "print('Dimensão de a2:', a2.ndim)\n",
        "print('Dimensão de a3:', a3.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensão de a0: 0\n",
            "Dimensão de a1: 1\n",
            "Dimensão de a2: 2\n",
            "Dimensão de a3: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXGd2r3ra0X9"
      },
      "source": [
        "Transforme uma lista em um array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "497CDTd9awol"
      },
      "source": [
        "lista = [1, 2, 3, 4]\n",
        "array = np.array(lista)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6PBFagAa7VP"
      },
      "source": [
        "Arrays são feitos para se trabalhar com álgebra linear. Veja a diferença quando multiplicamos cada elemento por um escalar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbhXL49ibF50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d50caf94-d885-4d84-a47a-766c9a4d9d35"
      },
      "source": [
        "print(2*lista)\n",
        "print(2*array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 2, 3, 4, 1, 2, 3, 4]\n",
            "[2 4 6 8]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7VxSD9DbJYl"
      },
      "source": [
        "Similarmente, podemos realizar as demais operações com arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRPNPtIibP3s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "2fc14ccd-e3f2-4438-d001-b5a86bc77fcd"
      },
      "source": [
        "print(array + array)\n",
        "print(array * 2)\n",
        "print(array / 3)\n",
        "print(array ** 2)\n",
        "print(array // 2)\n",
        "print(array % 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2 4 6 8]\n",
            "[2 4 6 8]\n",
            "[0.33333333 0.66666667 1.         1.33333333]\n",
            "[ 1  4  9 16]\n",
            "[0 1 1 2]\n",
            "[1 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJDDMrZuKQ_b",
        "outputId": "199bf1ec-c9d8-41ae-e0f8-ab26cef60102"
      },
      "source": [
        "np.random.choice([1,2,3,4,5], 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 4, 1, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6C1YIImKRc7",
        "outputId": "a04f0ece-ac5a-436e-e08a-cdbdd2c21af4"
      },
      "source": [
        "np.arange(0, 1, 0.01)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n",
              "       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n",
              "       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n",
              "       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n",
              "       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n",
              "       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n",
              "       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n",
              "       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n",
              "       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n",
              "       0.99])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T7G_f_0Kl52"
      },
      "source": [
        "np.all()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-XtTYNtKnGh"
      },
      "source": [
        "np.any()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rgc2tLUFwYCs"
      },
      "source": [
        "## **Funções Importantes** <a name=\"func\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Usm30s_ZUPHX"
      },
      "source": [
        "O comando **input** serve para se comunicar com o usuário enquanto se executa um programa. Veja o exemplo abaixo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiBIg2DvUWCN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "141f15fe-481b-4f69-bc1f-853dc318ceb4"
      },
      "source": [
        "x = input('Digite o seu nome: ')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Digite o seu nome: Ricardo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8Hlz87UUb1n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "279a1055-7066-43ad-a474-9b1b7d84a45a"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ricardo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vW4v9HdiUe6B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "08843126-1de2-4d5c-fd95-407c486129c1"
      },
      "source": [
        "y = input('Digite a sua idade: ')\n",
        "print('\\nA idade declarada foi {}'.format(y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Digite a sua idade: 33\n",
            "\n",
            "A idade declarada foi 33\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIAv3lqpU1CE"
      },
      "source": [
        "Mas note que os inputs são sempre no format string. Se quisermos usá-lo numericamente, temos que converter as entradas adequadamanente. Em geral usamos a função **int** para inteiros ou **float** para números reais. Você pode usar a função float para números que são inteiros também."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZU8B0hwVQoK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "483b7552-e9ee-47ad-fe82-e3a8df31f26c"
      },
      "source": [
        "y = int(input('Digite a sua idade '))\n",
        "\n",
        "print('\\nSe você já fez aniversário esse ano, seu ano de nascimento foi {}.'.format(2020 - y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Digite a sua idade 33\n",
            "\n",
            "Se você já fez aniversário esse ano, seu ano de nascimento foi 1987.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJ82dttbW44T"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZBxaKUswYSg"
      },
      "source": [
        "A função **range** cria um objeto capaz de gerar listas de valores entre dois números. \n",
        "\n",
        "Ela não gera a lista em si, mas um gerador de lista pra quando ela for utilizada. \n",
        "\n",
        "**Essa função trabalha com intervalos semi-abertos do tipo [ , )**. Veja os exemplos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4N5FABWW-6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5432e8ed-36f1-48bb-fbc4-23bacd63b59f"
      },
      "source": [
        "range(4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "range(0, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umj3TALlXBJG"
      },
      "source": [
        "Para listar os valores da função range, utilize o comando **list**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzLHaZmOXLIm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4933f225-dfab-48ed-e2ff-2c7dbb8ba6dd"
      },
      "source": [
        "list(range(4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SK-6cuaIXJbU"
      },
      "source": [
        "Use-a com dois parâmetros para definir o começo e o fim da lista. Note que o primeiro valor é incluido, mas o último não, pois o intervalo dela é semi-aberto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbgK2M_CXZSp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d08b8e19-0de4-4de4-e580-fbf441da1ff6"
      },
      "source": [
        "list(range(3, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5VMnOFlXfBB"
      },
      "source": [
        "Você ainda pode usar um terceiro parâmetro, que é o passo da lista. Ele deve ser inteiro e representa o salto de um número para o outro."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXI4nmWTXnYP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "68a0934a-6bd0-44c4-ed23-e8964ff64cc5"
      },
      "source": [
        "list(range(2, 21, 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmqGMlkRX5fA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0e3ac1c1-198e-4169-829b-bd6ea4d03d68"
      },
      "source": [
        "list(range(10, 101, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgAeaNFnaz6Z"
      },
      "source": [
        "A função **list** também pode ser utilizada para quebrar uma string em caracteres dentro de uma lista, veja:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsH_I9yya7vQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5921f70b-b120-419f-9d5e-30c97edd56a9"
      },
      "source": [
        "list('Python')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['P', 'y', 't', 'h', 'o', 'n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "810gugS8vlH-"
      },
      "source": [
        "## **Iteradores FOR e WHILE** <a name=\"iter\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuwTyZqyvrFX"
      },
      "source": [
        "Utilizamos a estrutura de repetição FOR sempre que desejamos repetir um certo pedaço de código, com alterações ou não, por um número pre-determinado de vezes.\n",
        "\n",
        "Veja os exemplos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9ZLbdPTeYa4"
      },
      "source": [
        "for variante in lista_de_variações:\n",
        "    codigo a ser repetido\n",
        "\n",
        "#esquema geral do FOR, não executar esse bloco"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMlptGxbel3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "fa8bb0df-d383-43a0-ceb6-4cd73068755b"
      },
      "source": [
        "for i in ['banana', 'mamão', 'abacate']:\n",
        "    print('Eu gosto de {}'.format(i))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Eu gosto de banana\n",
            "Eu gosto de mamão\n",
            "Eu gosto de abacate\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9ZcdJy1vrRH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "faa31159-c022-4e97-d914-e2784c1fb836"
      },
      "source": [
        "for i in range(10):\n",
        "    print(i*'*')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*\n",
            "**\n",
            "***\n",
            "****\n",
            "*****\n",
            "******\n",
            "*******\n",
            "********\n",
            "*********\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mhqfzfGfnbS"
      },
      "source": [
        "Problema: Se eu tomar dois números inteiros no intervalo [1, 10], qual a probabilidade aproximada da soma dos números ser maior que 10?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H98ra85ke7bY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e70df48b-34c7-4ef9-f6ca-53082c9b7bd0"
      },
      "source": [
        "from random import randint\n",
        "lista = []\n",
        "replicas = 10000\n",
        "\n",
        "for i in range(replicas):\n",
        "    if randint(1,10) + randint (1,10) > 10:\n",
        "        lista.append(True)\n",
        "    else:\n",
        "        lista.append(False)\n",
        "\n",
        "prob = sum(lista)/replicas\n",
        "\n",
        "print('A probabilidade aproximada é {}%'.format(100*prob))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A probabilidade aproximada é 54.52%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e63f82migunw"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCsKAKnogvoL"
      },
      "source": [
        "O iterador WHILE usamos quando queremos repetir código sem saber de antemão quando este deve ser interrompido. Ele será finalizado quando alguma condição for satisfeita. \n",
        "\n",
        "Por isso, tome cuidado em não escrever um WHILE infinito. Você terá que cancelar a execução do código para pará-lo.\n",
        "\n",
        "Veja a estrutura geral"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZq7UqmPhBQ_"
      },
      "source": [
        "while condicao_verdadeira:\n",
        "    codigo repetido\n",
        "    if condicao:\n",
        "        break\n",
        "        \n",
        "#esquema geral do FOR, não executar esse bloco"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlNNJ5xAhgBG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "ccb58d55-288a-4533-d169-977770dd1a28"
      },
      "source": [
        "i = 1\n",
        "while i <= 10:\n",
        "    print(i * '*') \n",
        "    i += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*\n",
            "**\n",
            "***\n",
            "****\n",
            "*****\n",
            "******\n",
            "*******\n",
            "********\n",
            "*********\n",
            "**********\n",
            "*\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZwNhK1bidv-"
      },
      "source": [
        "Código equivalente utilizando o comando break para interromper o loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5sqpDODiNIB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "c63468b9-6742-40cc-a844-ea7071949fc2"
      },
      "source": [
        "i = 1\n",
        "while True:\n",
        "    print(i * '*') \n",
        "    i += 1\n",
        "    if i == 10:\n",
        "        break\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*\n",
            "**\n",
            "***\n",
            "****\n",
            "*****\n",
            "******\n",
            "*******\n",
            "********\n",
            "*********\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_vENC2cim9V"
      },
      "source": [
        "Exemplo de um **while** infinito"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OSTl1SQiqso"
      },
      "source": [
        "while True:\n",
        "    print('Python é legal demais!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAOdZjiQiyJ8"
      },
      "source": [
        "O **while** também aceita o comando **else** no após o seu fim. Algo para executar após o break acontecer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9vxNRKOi9zH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "b515dc9e-8416-4b66-fec4-d946b464e8f2"
      },
      "source": [
        "i = 1\n",
        "while i <= 10:\n",
        "    print(i * '*') \n",
        "    i += 1\n",
        "else:\n",
        "    print('*')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*\n",
            "**\n",
            "***\n",
            "****\n",
            "*****\n",
            "******\n",
            "*******\n",
            "********\n",
            "*********\n",
            "**********\n",
            "*\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpNvv45sE3Vi"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTybxsEjYEko"
      },
      "source": [
        "## **PROJETO: Álbum One Piece 2021**\n",
        "- Total de cromos: **212**.\n",
        "- Preço do livro ilustrado capa brochura: **R\\$ 10,00**.\n",
        "- Envelope com 4 cromos: **R\\$ 4,00**.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8L_o_sWYFr9o"
      },
      "source": [
        "\n",
        "### **SUPOSIÇÕES**\n",
        "1. Todas as figurinhas tem igual probabilidade de serem sorteradas.\n",
        "2. Um pacotinho é comprado por vez até que se complete o álbum.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPqI87X_Frc3"
      },
      "source": [
        "\n",
        "### **ALGORITMO**\n",
        "1. Comprar um pacotinho de figurinhas (4 figurinhas cada, que podem ser repetidas);\n",
        "2. Colar no álbum e verificar se o álbum está completo;\n",
        "3. Caso esteja incompleto, comprar mais um pacotinho, caso contrário, terminar.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5cBz-uZDI8a"
      },
      "source": [
        "\n",
        "### **PERGUNTAS**\n",
        "1. Qual o valor médio investido para completar o álbum nessas condições?\n",
        "2. Quantos pacotes são necessários comprar, em média, para completar o álbum?\n",
        "3. Qual é a distribuição empírica do valor investido para completar o álbum?\n",
        "4. Qual a probabilidade de se gastar menos que R\\$1500,00 para completar o álbum?\n",
        "5. Qual a probabilidade de se gastar mais do que a média para completar o álbum?\n",
        "6. Qual é um intervalo de confiança de 95% para o gasto ao se completar o álbum?\n",
        "7. Qual o valor médio gasto caso se esteja completando o álbum com mais um amigo? \n",
        "8. Qual o valor médio gasto caso se esteja completando o álbum com mais dois amigos? \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2l9BcKaV_eX"
      },
      "source": [
        "n_album = 212\n",
        "preco_pacote = 4\n",
        "cromos_por_pacote = 4\n",
        "preco_album = 10 \n",
        "simulacoes = 1000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xB0munEeV_n_"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# representação do álbum\n",
        "album = np.zeros(n_album) \n",
        "\n",
        "# representação do pacote de figurinhas\n",
        "pacotinho = np.random.choice(range(n_album), cromos_por_pacote)\n",
        "pacotinho\n",
        "\n",
        "# 'colando' as figurinhas obtidas no álbum\n",
        "for i in pacotinho:\n",
        "    album[i] += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZA98qWkY6eS",
        "outputId": "115776ef-4838-4f3a-f8ae-626c633bf5a8"
      },
      "source": [
        "# comprando figurinhas até completar o álbum\n",
        "def SimulaAlbum():\n",
        "    album = np.zeros(n_album) \n",
        "    pacotes = 0\n",
        "    while not np.all(album >= 1):\n",
        "        pacotinho = np.random.choice(range(n_album), 4)\n",
        "        pacotes += 1\n",
        "\n",
        "        for i in pacotinho:\n",
        "            album[i] += 1\n",
        " \n",
        "    valor_gasto = preco_album + preco_pacote * pacotes \n",
        "\n",
        "    return valor_gasto, pacotes\n",
        "\n",
        "SimulaAlbum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1026, 254)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1khZHjQoJhg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b28f85d-cc3e-4c80-bd19-d68576bda7cf"
      },
      "source": [
        "def Simulacao(simulacoes = 1000, silencio = False):\n",
        "    valores = []\n",
        "    for i in range(simulacoes):\n",
        "        valores.append(SimulaAlbum()[0])\n",
        "        if not silencio:\n",
        "            if (i+1) % 100 == 0:\n",
        "                print('Simulação: ', i+1, '/', simulacoes)\n",
        "    return valores\n",
        "\n",
        "sim = Simulacao(simulacoes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Simulação:  100 / 1000\n",
            "Simulação:  200 / 1000\n",
            "Simulação:  300 / 1000\n",
            "Simulação:  400 / 1000\n",
            "Simulação:  500 / 1000\n",
            "Simulação:  600 / 1000\n",
            "Simulação:  700 / 1000\n",
            "Simulação:  800 / 1000\n",
            "Simulação:  900 / 1000\n",
            "Simulação:  1000 / 1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWgOBRkHqoKk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c28ae3f1-4ae3-42ee-cf94-759ef071f10e"
      },
      "source": [
        "#questões 1 e 2\n",
        "print('O valor médio gasto foi:', round(np.mean(sim), 2))\n",
        "print('O numero de pacotes médio foi:', round((np.mean(sim) - preco_album)/preco_pacote, 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O valor médio gasto foi: 1271.35\n",
            "O numero de pacotes médio foi: 315.34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "5Okpk1hpdsL1",
        "outputId": "231643a2-b308-4499-bb57-a674699567dd"
      },
      "source": [
        "#questão 3\n",
        "import matplotlib.pyplot as plt\n",
        "plt.hist(sim, bins = 20, density = True, color = 'royalblue', edgecolor = 'black')\n",
        "plt.title('Distribuição Empírica do Valor Gasto para Completar o Álbum')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXppupwcGvGz",
        "outputId": "25b84fdd-aa69-40e2-aa98-80db9c73f62f"
      },
      "source": [
        "# questoes 4 e 5\n",
        " \n",
        "prob1 = sum(np.array(sim) < 1500)/simulacoes\n",
        "prob2 = sum(np.array(sim) > np.array(sim).mean())/simulacoes\n",
        " \n",
        "print('A probabilidade de se gastar menos de 1500 é {:.2f}%'.format(prob1*100))\n",
        "print('A probabilidade de se gastar mais do que a média é {:.2f}%'.format(prob2*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A probabilidade de se gastar menos de 1500 é 83.20%\n",
            "A probabilidade de se gastar o dobro da média é 42.20%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIOgRcqAHKqR",
        "outputId": "ae3ffe61-2fa0-4bda-fe77-eba09e22c135"
      },
      "source": [
        "# questao 6\n",
        "\n",
        "qts = np.quantile(sim, [0.025, 0.975])\n",
        "print('O intervalo de confiança de 95% para o gasto total é ({:.2f}, {:.2f})'.format(qts[0], qts[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O intervalo de confiança de 95% para o gasto total é (878.00, 1954.60)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLhYEJ6Ctddf"
      },
      "source": [
        "### **E quando estamos trocando figurinhas na praça?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqjOg8hEuCur"
      },
      "source": [
        "Vamos ajustar a nossa função que simula o completamento de um álbum, para funcionar para uma quantidade variável de álbuns, representando assim uma estratégia de completar o álbum em grupo. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gP4ji2LJtztb",
        "outputId": "17cbd69a-48f9-4486-a692-6f14eae1c778"
      },
      "source": [
        "# comprando figurinhas até completar o álbum\n",
        "def SimulaAlbum(qtde_albuns = 1):\n",
        "    album = np.zeros(n_album) \n",
        "    pacotes = 0\n",
        "    while not np.all(album >= qtde_albuns):\n",
        "        pacotinho = np.random.choice(range(n_album), 4)\n",
        "        pacotes += 1\n",
        "\n",
        "        for i in pacotinho:\n",
        "            album[i] += 1\n",
        " \n",
        "\n",
        "    valor_gasto = qtde_albuns*preco_album + preco_pacote * pacotes \n",
        "    valor_gasto_por_album = valor_gasto/qtde_albuns\n",
        "\n",
        "    return valor_gasto, pacotes, valor_gasto_por_album\n",
        "\n",
        "SimulaAlbum(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1536, 379, 768.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApA_PH4MH8ri",
        "outputId": "d6cfd8fa-a8c3-44f0-f0e6-dd9eefd9d942"
      },
      "source": [
        "def Simulacao(qtde_albuns, simulacoes = 1000, silencio = False):\n",
        "    valores = []\n",
        "    for i in range(simulacoes):\n",
        "        valores.append(SimulaAlbum(qtde_albuns)[2])\n",
        "        if not silencio:\n",
        "            if (i+1) % 100 == 0:\n",
        "                print('Simulação: ', i+1, '/', simulacoes)\n",
        "    return valores\n",
        "\n",
        "sim = Simulacao(qtde_albuns = 2, simulacoes = 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Simulação:  100 / 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIumjhsGHmfk",
        "outputId": "ddfca204-7f4b-4025-b0bd-bbbc24ff36c4"
      },
      "source": [
        "# questões 7 e 8\n",
        "\n",
        "sim2 = Simulacao(2, 100, silencio = True) \n",
        "c2 = np.array(sim2).mean() \n",
        "\n",
        "sim3 = Simulacao(3, 100, silencio = True) \n",
        "c3 = np.array(sim3).mean()  \n",
        "\n",
        "print('Custo médio entre 2 amigos:', round(c2, 2) )\n",
        "print('Custo médio entre 3 amigos:', round(c3, 2) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Custo médio entre 2 amigos: 857.6\n",
            "Custo médio entre 3 amigos: 719.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hg-vAjb4hjd_",
        "outputId": "30194f64-8738-4098-d963-574ee4195694"
      },
      "source": [
        "def SimulacaoAmigos(amigos = 40, simulacoes = 10):\n",
        "    medias = []\n",
        "    amigos = list(range(1, amigos + 1, 1))\n",
        "    for i in amigos: \n",
        "        t = time.time()\n",
        "        aux = Simulacao(i, simulacoes, silencio = True)\n",
        "        tempo = round(time.time() - t, 2)\n",
        "        print('Tempo total de {}s para a simulação com {} amigo(s)'.format(tempo, i))\n",
        "        medias.append(np.array(aux).mean())\n",
        "    return amigos, medias\n",
        "\n",
        "sim_amigos = SimulacaoAmigos(amigos = 10, simulacoes = 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo total de 0.16s para a simulação com 1 amigo(s)\n",
            "Tempo total de 0.24s para a simulação com 2 amigo(s)\n",
            "Tempo total de 0.33s para a simulação com 3 amigo(s)\n",
            "Tempo total de 0.26s para a simulação com 4 amigo(s)\n",
            "Tempo total de 0.26s para a simulação com 5 amigo(s)\n",
            "Tempo total de 0.26s para a simulação com 6 amigo(s)\n",
            "Tempo total de 0.51s para a simulação com 7 amigo(s)\n",
            "Tempo total de 0.48s para a simulação com 8 amigo(s)\n",
            "Tempo total de 0.36s para a simulação com 9 amigo(s)\n",
            "Tempo total de 0.68s para a simulação com 10 amigo(s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "8M-zC-gjl79-",
        "outputId": "66694cf5-e9cc-4de9-9dc3-91dcb23a2b30"
      },
      "source": [
        "def GraficoCustoAmigos(simulacao):\n",
        "\n",
        "    minimo_possivel = math.ceil(n_album/cromos_por_pacote)*preco_pacote \n",
        "\n",
        "    plt.figure(figsize=(18, 6))\n",
        "    plt.bar(simulacao[0], simulacao[1], width = 0.5, color = 'royalblue')\n",
        "    plt.xticks(simulacao[0])\n",
        "    plt.axhline(y = minimo_possivel, linestyle = 'dashed', color = 'blue')\n",
        "    plt.title('Custo médio aproximado para\\n completar o álbum em função do tamanho\\n do grupo de pessoas trocando figurinhas')\n",
        "    plt.show()\n",
        "\n",
        "GraficoCustoAmigos(sim_amigos)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABBsAAAGYCAYAAAADXuoVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVhV5f7//9cWZEYRAQ1yxgkVIRHHABU/apbHOmaOOdYpnJss07RyOn2PSZnVSVNLs3K2QS1xLlJRxAFUnMsj5YA5z9y/P/q5jlsmh21bO8/HdXldrnvd6x7e697WfnOvhc0YYwQAAAAAAOAgRZw9AAAAAAAA8NdCsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4FMkGAADuUWfOnNGbb76pHTt2OHsoAAAAdkg2AABwj+rRo4f279+vatWqFVrXZrNpxowZ+R7/FY0YMUKhoaFO67979+6Kj493Wv8AADgTyQYAwD3v2LFjeumll1S1alV5eHgoKChIMTEx+vTTT3X58mWH9ePq6qpp06Y5rL3b8dZbb+ny5cv66KOPbun6rKwstWvXzsGjuru88MILWrt2rbOHAQDA/yRXZw8AAIDb8csvv6hx48ZydXXVG2+8ocjISBUtWlTJycn617/+pfDwcEVERDh7mA730ksv3db1pUuXdtBIHO/ixYtyc3O77XZ8fHzk4+PjgBH99Vy6dElFixZ19jAAAH9h7GwAANzTEhISdOHCBaWmpqpz584KCwtT5cqV1a1bN23cuFGVK1eWJMXFxal37952144cOVLly5e3jtPT09WiRQv5+fnJ29tb1atX1/Tp0yVJ5cuX15UrV9SjRw/ZbDbZbDbrukWLFqlOnTpyd3dXUFCQEhISdObMmQLHbbPZNGHCBD3xxBPy9vZW2bJlNWfOHJ04cUKdO3eWr6+vKlasqLlz59pd99tvv6l79+4KDAyUr6+vGjVqpNWrV9vVWbFihcLDw+Xh4aHw8HCtWLEiz/6vfYwiKytLHTp0kJ+fnzw9PRUXF6cNGzYUOIfU1FS1atVKQUFB8vHxUd26dbVkyRK7OuXLl9err76q3r17q1ixYgoICNCQIUOUk5NjV2fo0KFKSEhQyZIl9eCDDxYa11mzZsnNzU3r16+32vn000/l6empLVu2SMr9GMXV41mzZqly5cry8vJS27ZtdfLkSc2bN09Vq1aVr6+v2rVrpxMnTtzUPLOzs617WapUKQ0dOlTGGLs6ly5d0ssvv6yQkBC5ubkpLCxMM2fOLDDG06ZNk6urq5KSklSjRg15eHioXr16SktLs+ocP35cXbp0UdmyZeXp6amqVatq3Lhxdv1ffaRjwoQJKl++vNzd3XXu3DktXbpUcXFx8vf3V/HixRUbG2sXUwAAbhXJBgDAPSs7O1uLFi1S3759Vbx48VznixYtKm9v7xtur2PHjipZsqSSk5O1detWvf322ypRooQkKSUlRS4uLkpMTFRWVpaysrIkSVu2bFGbNm0UExOjzZs365NPPtE333yjZ555ptD+Ro0apYceekibN2/Www8/rK5du6pDhw5q3ry5Nm3apNatW+vJJ5/UsWPHJEnnzp1TkyZNdOrUKS1evFibNm3SQw89pObNm2v79u2SpEOHDunhhx9WnTp1lJqaqnHjxmnAgAEFjsMYo7Zt22rHjh365ptvtH79epUqVUrNmzfX0aNH873u5MmTeuKJJ7RixQqlpqaqRYsWatOmjTIzM+3qTZgwQcHBwUpJSdH48eP1zjvvaMKECXZ13n33XQUFBemnn37S1KlTC41r+/bt1a1bN3Xs2FEnT55UZmam+vTpo3Hjxik8PDzfMWdlZemTTz7R3LlztXjxYv34449q166dJk+erFmzZmnx4sVas2aNRo8efVPz7NWrlzZu3Kivv/5ay5cv1/79+zV//ny7vocMGaJJkyYpMTFR27ZtU5cuXdSlSxctW7aswPuTk5Ojl156Se+//77Wr1+vwMBAtW7dWufOnZMkXbhwQTVr1tSCBQuUkZGhYcOGafjw4bke+Vm/fr2WL1+uhQsXavPmzXJzc9Pp06eVkJCgn376ScnJyapcubJatmxprTkAAG6ZAQDgHrVu3TojycydO7fQurGxsaZXr152ZW+++aYpV66cdVysWDEzderUfNtwcXHJdb5Lly6mbt26dmULFiwwNpvN7N+/P9+2JJkBAwZYx4cPHzaSTN++fa2y7OxsI8l8/fXXxhhjpk6dakJCQsylS5fs2mrSpInV1quvvmrKli1rV+frr782ksz06dPt+r96nJSUZCSZ9PR06/z58+dN6dKlzeuvv57vHPISHh5uRo4caR2XK1fONG7c2K7OK6+8Yu6//367Ok2bNrWrcyNxPXPmjAkLCzOPP/64iYiIMG3btrWrP3z4cFOpUiW7YxcXF3PkyBGrLCEhwRQpUsQcPnzYKuvfv7+pU6fODc9z165dRpL5/vvvrfMXLlwwwcHBplmzZtZY3dzczMSJE+3aadu2rWnSpEm+/UydOtVIMklJSVZZdna28fb2NpMnT873uv79+5v4+HjruFu3bqZ48eLm1KlTBc7rypUrxs/Pz8yYMaPAegAAFIadDQCAe5a5bpv67XrhhRfUu3dvxcXFacSIEUpNTS30mvT0dMXExNiVxcbGyhijjIyMAq+tXbu29ffAwEC5uLjY/VS+RIkScnNz0+HDhyX9sbvi119/lZ+fn/U+Ah8fH61Zs0a7du2SJGVkZCg6Olqurv99LVPjxo0LnUPJkiUVFhZmlbm7u6tevXpKT0/P97ojR44oISFB1apVs8aUnp6uAwcO2NVr0KCB3XGjRo108OBBnTx50iqLjo7ONabC4url5aUvv/xS8+bN0+HDh/Xxxx8XOE9JCgkJUUBAgHVcunRplS5dWoGBgXZlV2N+I/O8Op6GDRta17i5ualu3brW8e7du3Xx4sU851RQjK+6NoYlSpRQ9erVretycnI0duxYRUREKCAgQD4+Pvrwww9z3Yfq1avneofFvn371LVrV4WGhqpYsWIqVqyYTpw4ketaAABuFi+IBADcsypXrqwiRYooIyNDjz32WIF1ixQpkucz9NcaNmyYOnfurCVLlmj58uUaPXq0XnrpJY0cOdLhY5eU5wv6ri+z2WzW+w1ycnJUvXr1XNvzpT++eP/Zunfvrp9//llvvfWWKlSoIE9PT3Xo0EEXL1686bZu5nGXa/3www+SpBMnTujIkSPy9/cvsH5e8S0o5pJj53knjBs3TmPGjNH48eMVGRkpX19fjR8/Xt9++61dvbxi/PDDDysgIEATJ05UmTJl5ObmpsaNG981cwMA3LvY2QAAuGf5+/urVatWeu+99+xe6HfVpUuXrBcKBgUF6dChQ3bn89q5ULFiRSUkJGjOnDl644039MEHH1jn3NzcdOXKFbv6NWrUyPWCxlWrVslms6lGjRq3PLe8REVFae/evSpWrJhCQ0Pt/gQHB0uSwsLCtH79ertx/vjjjwW2W6NGDR07dsxuJ8aFCxe0bt061axZM9/rVq9erYSEBLVp00a1atXSfffdp7179+aqd/2vn0xOTlZISIiKFStW4JgKi+u2bdv03HPPafLkyYqPj1eHDh104cKFAud6Kwqb59UdIcnJyVbZxYsXlZKSYh2HhobK3d09zzkVFOOrro3h77//ru3bt1v9rl69Wi1btlTPnj0VGRmp0NBQa6dLQa7e85dfflktWrRQWFiYPDw87HZ1AABwq0g2AADuae+//76KFi2qOnXqaObMmcrIyNDu3bs1Y8YMRUVFWV+64uPjlZSUpNmzZ2v37t0aO3as1qxZY7Vz+vRp9enTR8uXL9e+ffu0adMmLVmyxO7RggoVKmjFihU6dOiQ9eLEF198UampqRo0aJB27NihJUuWqF+/furcubPKli3r0Ll27txZFSpUUOvWrfX9999r//79WrduncaMGaMFCxZIkp599lkdOXJETz/9tLZv365ly5bp1VdfLbDdpk2bKjo6Wp06ddKPP/6obdu26cknn9T58+f17LPP5ntd1apV9dlnn2nr1q1KS0tTx44dcyVjJCktLU0jRoxQZmamZs6cqXfeeUfPP/98gWMqLK7nz59Xx44d1bZtW3Xv3l1TpkzR0aNHb/tXgt7KPENDQ9WmTRv16dNHK1asUEZGhnr37q1Tp05Zdby8vNS/f38NGzZMs2fPVmZmpkaPHq2FCxdqyJAhBfZvs9n00ksvafXq1dq6dauefPJJ+fr6qlOnTtb4Vq5cqRUrVigzM1NDhw7VunXrCp1XiRIlFBgYqEmTJikzM1M//fSTOnbsKE9Pz1uMFAAA/0WyAQBwTytbtqxSU1PVtm1bjRgxQg888IAaNmyoSZMm6cUXX7R+atytWzf16dNHffr0UVRUlH755Rf179/fasfV1VXHjx9Xr169VL16dbVo0UKlSpWy+9WE48aN08aNG1W+fHnrGf/w8HB99dVXWr16tWrXrq2uXbuqdevW+vDDDx0+Vw8PD61atUpRUVHq0aOHqlSposcee0zr169XuXLlJP3xToKvv/5a69evV0REhAYMGKC33367wHZtNpsWLFigatWqqXXr1qpbt65+/fVXLV261O79BtebOnWqcnJyFB0drbZt26ply5Z27ym4ql+/fjpw4ICioqLUr18/9e3bt9DfkFFYXAcNGqQzZ85Yx/7+/po5c6bef//9XI8P3K4bmeeUKVMUERGhhx9+WLGxsQoJCdGjjz5qV2fUqFF66qmnNHDgQNWsWVMzZszQjBkz1KxZswL7L1KkiEaPHq1//OMfioqK0q+//qpvv/3WenRm2LBhio2N1d/+9jc1aNBAx48ft1vbBbU7e/Zs7dmzR+Hh4erevbsGDhyo++677yYjBABAbjbj6LdrAQAA/P/Kly+v3r17a+jQoc4eyj1p2rRp6t27ty5fvuzsoQAAcFPY2QAAAAAAAByKZAMAAAAAAHAoHqMAAAAAAAAOxc4GAADuIjNnztT8+fOdPQwAAIDbQrIBAHDPGTFihEJDQ509DIf77rvv9Nprr6lBgwZ25dfP9686/5sxe/ZsVapUSS4uLurevfuf2ndaWpqCgoL097//XUeOHFH16tXvWF/ly5fXyJEj71j7dzPWOQDc20g2AAD+J/Tu3VtxcXHOHka+/vOf/2jgwIFatGiRSpcu7ezh3NWuXLminj17qn379vr555/1zjvv/Kn9f/XVVxoyZIiioqIUHR2tZ5555k/t/2bd7WsfAPDX5OrsAQAAcK+5dOmSihYt6tA2Q0JCtH37doe2+VeVlZWl06dP66GHHlJISMif3v9rr71m/f2VV1750/sHAOBewM4GAECeDh8+rB49eqhUqVLy8PBQ1apVNWXKFOv82rVrFRMTI09PT5UoUUKdOnXS4cOHrfNXt0DPmjVLlStXlpeXl9q2bauTJ09q3rx5qlq1qnx9fdWuXTudOHHCuq579+6Kj4/X+PHjFRISIi8vLz3++OPKzs4ucLxLly5Vo0aN5OnpqZCQEPXo0UPHjh2zxvLxxx9r1apVstlsstlsmjZtmiTpnXfeUUREhHx8fFS6dGl16NBBWVlZVrsrV66UzWbTt99+q8aNG8vDw0OTJ0/OcwyXLl3Syy+/rJCQELm5uSksLEwzZ84scNzHjx9Xly5dVLZsWXl6eqpq1aoaN26cbuT9zTNnzlTFihXl4eGh5s2ba//+/da5vLag//DDD7LZbFa9adOmydXVVStWrFCtWrXk6empuLg4HTp0SKtXr1ZkZKS8vb0VHx+v//znPwWO5dKlSxoxYoQqVKggDw8P1ahRQ//+97/t6thsNk2YMEFPPPGEvL29VbZsWc2ZM0cnTpxQ586d5evrq4oVK2ru3Ln59jNt2jSVKVNGkhQTEyObzaaVK1dac7nWwYMHrfPSf+/l0qVLFRMTIy8vL4WFhWnx4sV21xW09o0xeuqpp1SpUiV5enqqYsWKGjJkiC5cuGDXxieffKKwsDC5ubnp/vvv19ChQ3X58uUCY7h582Y1bNhQ7u7uqly5smbNmpWrTlZWljp06CA/Pz/rfm3YsCHfNh2x9hctWqQGDRrI09NTderUUXp6utLT09W4cWN5eXkpOjpaGRkZ1nU3sqavfs4/+ugjlStXTsWKFVObNm3022+/5ZrDwoULVa1aNXl7eysuLk67du2yO79o0SLVqVNH7u7uCgoKUkJCgs6cOVNgrAEAfwIDAMB1zp49a6pVq2YiIyPN0qVLzZ49e8x3331nPv/8c2OMMVlZWcbX19d07NjRbNmyxaxZs8bUqlXLPPjgg1Ybw4cPN15eXuahhx4ymzdvNitXrjQBAQGmefPmplWrViYtLc2sWbPGBAUFmZdeesm6rlu3bsbX19c88sgjZsuWLWbFihUmNDTUtG3b1q7tSpUqWcfLli0znp6e5t133zWZmZlm/fr1Ji4uzsTExJicnBxz6tQp06lTJ9OgQQOTlZVlsrKyzNmzZ40xxiQmJpqlS5eavXv3muTkZNOgQQMTExNjtb1ixQojyVStWtV89dVXZu/eveaXX37JM24vvPCC8ff3N7NmzTI7d+40o0aNMjabzSQlJeUb66ysLDNmzBizceNGs3fvXjNz5kzj4+NjpkyZku98r8a2UaNGJiUlxaxfv95ER0ebyMhIk5OTk+c1xhizZs0aI8ns27fPGGPM1KlTjc1mM7GxsWbt2rVm48aNJjQ01DRu3NjExsaan376yWzatMlUrVrVtG/fPt85XL1vtWrVMt99953Zu3ev+eKLL0zx4sXN5MmTrTqSTKlSpcy0adPMrl27zLPPPms8PDxMy5YtzdSpU82uXbtM3759jZeXlzl69Gie/Zw9e9asX7/eSDILFy40WVlZ5sKFC2bq1KnGxcXFru4vv/xiJJkVK1YYY/57L8PDw83ixYtNZmam6d69u/H19TXZ2dlW+wWt/StXrpghQ4aYtWvXmn379pmFCxea0qVLm9dee83q95tvvjFFihQxo0ePNjt37jRffPGF8fPzM0OHDs03fmfPnjXBwcHWZyM5OdlERUUZT09P8+abbxpjjMnJyTHR0dGmdu3aZs2aNWbLli2mffv2xs/Pzxw5ciTPdh2x9iMiIsyyZctMenq6qV+/vvVZT0pKMhkZGaZRo0YmOjrauu76NT19+nTj7e1tt6a7detmihUrZjp06GC2bt1qkpOTTfny5U2XLl2sOlfXeYsWLcyGDRtMWlqaeeCBB0zjxo2tOps3bzYuLi5m4MCBZvv27WbRokWmTJkydu0AAJyDZAMAIJfJkycbd3f3fL9UDx061ISEhJgLFy5YZWlpaUaSWbVqlTHmjy8KLi4udl+CEhISTJEiRczhw4etsv79+5s6depYx926dTPe3t7m999/t8q+++47I8ns2rXLavvaL9KxsbFm8ODBdmM8cOCAkWQ2bdpkjDGmV69eJjY2ttC5p6amGknm4MGDxpj/fuH69NNPC7zuzJkzxs3NzUycONGuvG3btqZJkyaF9nutgQMHmvj4eOs4r2TDtfEwxpidO3caSVZi40aTDdfGyBhj3nrrLSPJbNiwwSp7++23TcmSJfMd7969e43NZjPbt2+3K3/99ddN7dq1rWNJZsCAAdbx4cOHjSTTt29fqyw7O9tIMl9//XW+/e3bt89IMmvWrLHKbibZMHfuXKvOr7/+aiSZJUuWGGMKX/t5efvtt01oaKh13LhxY/P444/b1UlMTDQeHh52n5lrTZo0yXh7e1tJD2OM2bp1q5FkJRuSkpKMJJOenm7VOX/+vCldurR5/fXX8x3f7a79+fPnW3VmzZplJJk5c+ZYZfPmzTOSzKlTp/Jtu3///nZrulu3biYwMNCcP3/eKhs7dqwpXbq0dXz135Br/7344osvjM1mM+fOnTPGGNOlSxdTt25du74WLFhgbDab2b9/f6FzBgDcOTxGAQDIZePGjQoLC9P999+f5/n09HTVr19fbm5uVlnt2rVVvHhxpaenW2UhISEKCAiwjkuXLq3SpUsrMDDQruzaxy8kKSwsTMWLF7eOGzVqJEl2W7WvlZKSosTERPn4+Fh/wsLCJCnXluvrrVy5Ui1atFCZMmXk6+urxo0bS5IOHDhgVy86OrrAdnbv3q2LFy8qJibGrjw2NtYuJte7cuWKhg8frsqVK8vDw0M2m02JiYm5+r9eYGCg3WMSVapUUUBAQIF95cVms6lWrVrW8dWXU4aHh9uVHTt2TFeuXMmzjQ0bNsgYo6ioKLt7MHr06Fzxr127tt0cXFxc7PoqUaKE3Nzccq0JR4qIiLD+XqpUKbm4uFjb9wtb+5I0adIk1atXT6VKlZKPj49eeeUVu/uVnp6e5zo4f/689uzZk2ebGRkZql69ukqUKGGV1axZ0+5zkJ6erpIlS1prW5Lc3d1Vr169m77v0o2v/WvvWX7rQ5J1z3JycjR27FhFREQoICBAPj4++vDDD3O1W61aNbm7u1vHwcHBuR6jCA4Otvv3Ijg4WMYYq6/8Ym2MyfffCwDAn4MXRAIA7pjrX6Jos9nyLMvJybmtfnJycjR48GB17do117mCfrPDzz//rIceekhdu3bVa6+9poCAAB08eFDx8fG6ePGiXV1vb+/bGmN+3nrrLX344Yf64osvFBUVJV9fXw0ZMqTQdz0UpkiRIrne+3Dp0qU867m4uFjHNptNkv29u1p2fXtXXb1/ycnJ8vLysjt39dqr8nqxpiPWRJEiuX9+ktd8Jdklya660f5mz56tPn36aOzYsYqNjVWxYsU0e/Zsvfrqqzc1Xme7mbWf11rIq+xqDMeNG6cxY8Zo/PjxioyMlK+vr8aPH69vv/3Wrt3r74PNZsu1xvKqc21fAIC7FzsbAAC51KlTRxkZGTp48GCe52vUqKG1a9fafSnZvHmzTpw4oZo1a952/9u3b9fJkyet4+TkZEmy+4nutaKiopSenq7Q0NBcf3x8fCT98aXl+p/Mp6Sk6Ny5c0pMTFSjRo1UtWrVPF9QdyNCQ0Pl7u6u1atX25WvWrWqwJgkJyerSZMmatKkiXx9fe3mW5AjR47Y/ZQ8MzNTR48etWIUFBSkw4cP2805NTX1puZ0o+rUqSPpjy+w18e/UqVKd6TP6wUFBenKlSt29+9W5lvY2r/64sznnntOderUUeXKle1ezCn98fnIax14enrmG4+wsDBt375dv//+u1WWnp5u9/LUGjVq6NixY3Y/sb9w4YLWrVtX4Bq702v/eqtXr1bLli3Vs2dPRUZGKjQ0tNAdRrcqv1jbbDbVqFHjjvQJALgxJBsAALl07NhR5cqVU5s2bZSUlKR9+/Zp2bJl+vLLLyVJffv21cmTJ9W9e3dt27ZNP/zwg7p27aoHH3xQDz744G33b7PZ9OSTT2rbtm1avXq1+vTpozZt2uT67QpXvfHGG1q4cKGee+45paWlac+ePVqyZIl69eqlc+fOSZIqVKigHTt2KD09XUePHtWFCxdUuXJl2Ww2jRs3Tvv27dOCBQv0xhtv3NKYvby81L9/fw0bNkyzZ89WZmamRo8erYULF2rIkCH5XletWjUlJSVp+fLl2rlzpwYPHqy0tLQb6q9Hjx7asGGDNmzYoG7duikiIkLNmjWTJDVp0kRnz57Va6+9pj179mj27NmaOHHiLc2tMKGhoerZs6eeeuopTZ8+Xbt379bmzZs1ZcoU/fOf/7wjfV4vOjpavr6+evnll7Vr1y4tWbLklu5lYWu/atWq2rp1qxYuXKg9e/bonXfe0bx58+zaeOWVVzR37lyNHTtWmZmZmjVrlkaMGKHnn38+z10VktSpUyf5+vqqS5cu2rx5s9auXauePXvK09PTqtO0aVNFR0erU6dO+vHHH7Vt2zY9+eSTOn/+vJ599tl853Sn1/71qlatqpUrV2rFihXKzMzU0KFDtW7dOoe0fb0XX3xRqampGjRokHbs2KElS5aoX79+6ty5s8qWLXtH+gQA3BiSDQCAXLy8vKyfyHfo0EHVq1dXnz59rC/upUqV0vfff6+DBw+qbt26evjhh1WzZk3NmTPHIf1HR0ercePGat68uVq2bKlatWrZ/drN6zVp0kTLly/Xli1b9OCDDyo8PFyDBg2Sr6+vtd27V69eqlu3rho2bKjAwEB9/vnnCg8P14QJE/Tvf/9bYWFh+te//qXExMRbHveoUaP01FNPaeDAgapZs6ZmzJihGTNmWAmAvAwbNkzNmjXTo48+qoYNG+r06dPq379/oX3dd999evrpp9WuXTvrVxDOmzfP2mZetWpVTZo0SZ9//rlq1qypKVOmaPTo0bc8t8J89NFHGjRokEaNGqWwsDA1a9ZMn3zyiSpWrHjH+ryWv7+/Pv/8c61du1bh4eF688039dZbb910O9eu/ccff1wVK1a0W/v/+Mc/1LVrV/Xo0UORkZFat26dRowYYdfGQw89pClTpuiTTz5RzZo1NWjQICUkJGj48OEF9rto0SIdO3ZM0dHR6ty5swYNGqSgoCCrjs1m04IFC1StWjW1bt1adevW1a+//qqlS5favRvlen/G2r/WsGHDFBsbq7/97W9q0KCBjh8/fkNr+laEh4frq6++0urVq1W7dm117dpVrVu31ocffnhH+gMA3Dibye8BTAAAnKB79+46ePCgkpKSnD0UQC1btlRiYqKqVavm7KEAAHBPYWcDAADAdQ4dOqT9+/fLy8tLixcvdvZwAAC455BsAAAAuM5PP/2k6tWra+PGjWratKmzhwMAwD2HxygAAAAAAIBDsbMBAAAAAAA4FMkGAAAAAADgUCQbAAC3rXz58ho5cqSzh3FXISa4Ki4uTr17977tdmbPnq1KlSrJxcVF3bt318qVK2Wz2XTw4EEHjPLG2Gw2zZgx46au4bMAAP+bXJ09AAAAgOv17t1bu3fv1sqVK509lLvClStX1LNnT/Xt21d9+/aVj4+PPD09lZWVpaCgoD9tHFlZWfLz8/vT+gMA3LtINgAA7lk5OTkyxsjFxcXZQ4GTXLx4UW5ubs4exh2XlZWl06dP671mNDsAACAASURBVKGHHlJISIhVXrp06T+l/6tx/rP6AwDc+3iMAgBwUzZv3qyGDRvK3d1dlStX1qxZs3LVycrKUocOHeTn5ydPT0/FxcVpw4YNBbabk5OjIUOGKDAwUD4+PurQoYMSExPl6vrfvPiIESMUGhqqL7/8UtWqVZObm5syMzPVvXt3xcfH27U3Y8YM2Wy2XNfOnDlTFStWlIeHh5o3b679+/fbXffJJ58oLCxMbm5uuv/++zV06FBdvnz5tmNy+vRpDRgwQCEhIfLy8lJkZKTmzZtXYLs3OualS5eqUaNG8vT0VEhIiHr06KFjx45Z59PT09WiRQv5+fnJ29tb1atX1/Tp063zkydPVvXq1eXh4SF/f3/FxMTYbc1ftGiR6tSpI3d3dwUFBSkhIUFnzpyxzqempqpVq1YKCgqSj4+P6tatqyVLltiNceHChYqMjJSXl5f8/PwUHR2tTZs25Tvvjz/+WKtWrZLNZpPNZtO0adMk/bGN/91331WnTp1UvHhxde3aVdKN3beJEycqLCzMmsff//5369zMmTNVr149FS9eXAEBAWrdurUyMzOt8/v375fNZtOsWbP08MMPy8vLSxUrVrTGddWBAwfUsmVLeXp6qkyZMpowYUKu+Z06dUr/+Mc/FBgYKHd3d0VFRen777/PMxaSNG3aNJUpU0aSFBMTI5vNppUrV+b5GMWyZctUq1YteXh4KDw83Irh1Ucfrs7jhx9+sOsjNDRUI0aMsI7zi/P1j1HYbDa9//776tq1q3x9fXX//fdrzJgxueZw8eJFDRgwQP7+/ipVqpQGDRpkd3+WLl2quLg4+fv7q3jx4oqNjdX69evt2ihsnQIA7jIGAIAbdPbsWRMcHGxatWpl0tLSTHJysomKijKenp7mzTffNMYYk5OTY6Kjo03t2rXNmjVrzJYtW0z79u2Nn5+fOXLkSL5tjxs3znh7e5tPP/3UZGZmmnHjxpkSJUoYFxcXq87w4cONp6eniYmJMWvXrjU7d+40J0+eNN26dTPNmjWza2/69Onm2v/MDR8+3Hh5eZlGjRqZlJQUs379ehMdHW0iIyNNTk6OMcaYb775xhQpUsSMHj3a7Ny503zxxRfGz8/PDB069LZjEhcXZ2JjY82aNWvMnj17zL///W9TtGhRk5SUlG/bNzLmZcuWGU9PT/Puu++azMxMs379ehMXF2diYmKsOrVq1TIdO3Y06enpZs+ePWbRokXm66+/NsYYs2HDBuPi4mI++eQTs3//frNlyxYzadIk88svvxhjjNm8ebNxcXExAwcONNu3bzeLFi0yZcqUMV26dLHGuWLFCjN16lSzbds2s3PnTvPqq6+aokWLmp07dxpjjMnKyjJFixY1//znP83evXtNRkaG+eyzz8yWLVvynPepU6dMp06dTIMGDUxWVpbJysoyZ8+eNcYYI8n4+/ubCRMmmN27d5vMzMwbum+vvfaa8fb2NhMmTDA7d+40GzduNCNHjrTOT5kyxXz11Vdm9+7dJjU11TzyyCMmNDTUXLhwwRhjzL59+4wkU6FCBfPll1+aXbt2mVdeecW4uLhY88zJyTGRkZEmKirKrF271mzatMnEx8cbX19f06tXL6uvdu3amXLlypklS5aYjIwM079/f1O0aFGzffv2fNfY+vXrjSSzcOFCk5WVZS5cuGBWrFhhJFn36uDBg8bT09P06tXLpKenm6SkJBMZGWkkmenTp9vNY82aNXZ9VKpUyQwfPtw6zivOV8uvtnX1OCgoyHz00Udm9+7d5r333jOS7NZ1uXLljJ+fnxkzZozJzMw0X375pXF1dTWTJ0+26sybN898+eWXZseOHWbbtm2mV69epkSJEubo0aPGmMLXKQDg7kOyAQBwwyZNmmS8vb1Ndna2VbZ161YjyfpinZSUZCSZ9PR0q8758+dN6dKlzeuvv55v28HBwbm+1D/xxBO5kg02m80cOHDArt6NJhskmV27dlllO3futPti1LhxY/P444/btZOYmGg8PDysL523EpMVK1YYd3d38/vvv9td26NHD/O3v/0t74Dc4JhjY2PN4MGD7a47cOCAkWQ2bdpkjDGmWLFiZurUqXn2MW/ePFOsWDFz4sSJPM936dLF1K1b165swYIFxmazmf379+c79vDwcOvLfGpqqpFk9u3bl2/96/Xq1cvExsbmKpdkevbsaVdW2H07ffq08fDwMP/v//2/G+7/2LFjRpL54YcfjDH//ZI+btw4q87ly5eNj4+P+fDDD40xxixdutRIspIPxhhz+PBh4+HhYSUbdu3aZSSZb7/91q6/yMhI06NHj3zHk1eS4Ppkw5AhQ0y5cuXM5cuXrTqLFy++5WTD9XG+Wn59sqFfv352dapVq2Zefvll67hcuXLmkUcesavTsmVL06FDh3zne+XKFePn52dmzJhhjCl8nQIA7j48RgEAuGEZGRmqXr26SpQoYZXVrFlTxYsXt47T09NVsmRJhYWFWWXu7u6qV6+e0tPT82z3xIkTOnTokOrXr29X3qBBg1x1S5UqpbJly97S+AMDAxUaGmodV6lSRQEBAda40tPTFRMTY3dNbGyszp8/rz179uTZ5o3EJCUlRRcvXlRISIh8fHysPzNmzNCuXbtua8wpKSlKTEy0a/dq7K+2/cILL6h3796Ki4vTiBEjlJqaarXXvHlzVaxYURUqVFCHDh300Ucf6ejRo9b5/GJijFFGRoYk6ciRI0pISFC1atXk5+cnHx8fpaen68CBA5Kk8PBwtWjRQjVr1tSjjz6qd955R7/88kuB8y5IdHS03XFh9y09PV3nz5/X//3f/+XbZlpamh599FFVqFBBvr6+1hq7OoerIiIirL+7uLgoKChIv/32m6Q/1kJAQICqVKli1QkMDFTVqlWt46sxu368MTEx+X4+blRGRobq1q1r9w6TvD5DN+r6OOfn2phIUnBwsBWTG62zb98+de3aVaGhoSpWrJiKFSumEydOWPEvbJ0CAO4+JBsAAHeNa9+xkB9vb+9cZUWKFJExxq7s0qVLDhvX7crJyVHx4sWVlpZm9ycjI0OLFy++7bYHDx6cq+1du3apVatWkqRhw4YpMzNT7du317Zt21S/fn0NHTpUkuTj46MNGzZo/vz5qlKlij788EOFhoZq48aNNzyG7t27a82aNXrrrbe0Zs0apaWlKSIiQhcvXpT0x5fyxYsXa/ny5apbt67mzp2rKlWq6JtvvrmlOee1Bm7H2bNn9X//93+y2WyaOnWq1q9fr5SUFNlsNmsOV13/MkqbzaacnByHjud2FPYZKlLkj//1u5HPy43G+UZiUlidhx9+WD///LMmTpyotWvXKi0tTUFBQVb8HbFOAQB/LpINAIAbFhYWpu3bt+v333+3ytLT03XixAnruEaNGjp27Jj1E1xJunDhgtatW6eaNWvm2W7x4sUVHBysn376ya587dq1NzSuoKAgHTp0yK7s2p/eX3XkyBG7HQqZmZk6evSotROgRo0aWr16td01q1atkqenpypVqpRn3zcSk6ioKP3+++86f/68QkND7f4UtkujsDFHRUUpPT09V7uhoaHy8fGxrqtYsaISEhI0Z84cvfHGG/rggw+scy4uLoqJidEbb7yhjRs36r777tPMmTMLjInNZlONGjUkSatXr1ZCQoLatGmjWrVq6b777tPevXvtrrHZbIqOjtaQIUO0evVqxcbGaurUqfnO283NTVeuXCkwNlcVdt/CwsLk4eGR70sYt2/friNHjmjUqFGKi4tT9erVdfz48VxfyAsTFhamo0eP2u1WOXr0qHbu3Gk3Vkm5xrt69ep8Px83039KSopd3K7/DAUGBkqS3efl8OHD+s9//nNbfd+Oq/9evPzyy2rRooV1vw4fPmxXr6B1CgC4+5BsAADcsE6dOsnX11ddunTR5s2btXbtWvXs2VOenp5WnaZNmyo6OlqdOnXSjz/+qG3btunJJ5/U+fPn9eyzz+bb9vPPP6/ExER99tln2rVrlxITE/X999/f0G6H+Ph47dixQxMnTtSePXs0adKkPH8jhJeXl3r06KENGzZow4YN6tatmyIiItSsWTNJ0iuvvKK5c+dq7NixyszM1KxZszRixAg9//zz+f56xRuNSXx8vB577DEtWLBAe/fu1caNGzVhwgRNmjSpwLkVNuY33nhDCxcu1HPPPae0tDTt2bNHS5YsUa9evXTu3DmdPn1affr00fLly7Vv3z5t2rRJS5YssZIVCxcu1Pjx47Vx40b9/PPPWrBggX755Rfr/IsvvqjU1FQNGjRIO3bs0JIlS9SvXz917tzZSpRUrVpVn332mbZu3aq0tDR17NjR7gtvcnKy3nzzTa1bt04///yzli1bpi1bttg9anO9ChUqaMeOHUpPT9fRo0d14cKFfOsWdt98fHz0/PPPa8SIEZo4caIyMzO1efNm67cmlCtXTu7u7powYYL27NmjZcuWacCAATe09q7VrFkz1a5dW126dNH69euVlpamzp07q2jRoladSpUq6fHHH1dCQoK+++477dixQwMGDNC2bdv04osv3lR/10tISNBvv/2mZ599Vtu3b9eKFSv06quvSvrvjgdPT081atRIb731ljZv3qyNGzfqySeflLu7+231fTtKlCihwMBATZo0SZmZmfrpp5/UsWNHu89QYesUAHAXcu4rIwAA95rU1FRTv3594+bmZipWrGg+//xzU65cOetliMYYc+jQIfPEE0+Y4sWLGw8PDxMTE2NSUlIKbPfKlSvm5ZdfNiVLljTe3t7miSeeMKNGjTI+Pj5WneHDh5tKlSrlef3IkSNNcHCw8fb2Nh06dLDein/9tdOnTzflypUz7u7upmnTpmbv3r127UybNs1Uq1bNFC1a1AQHB5shQ4aYS5cu3XZMzp49awYPHmzKly9vihYtakqVKmVatGhhli1blm+7Nzrm1atXm2bNmhkfHx/j5eVlqlWrZgYMGGAuXbpkzp07Zzp27GjKly9v3N3dTWBgoGnfvr35+eefjTHGrFq1yjRp0sQEBAQYd3d3ExoaasaMGWPX/rfffmseeOAB4+bmZgICAswzzzxjTp8+bZ3fsmWLadCggfHw8DDlypUzEydONM2aNTPdunUzxhizbds206pVK1OqVCnj5uZmypYta1544YV8X7ppzB8vaGzVqpUpVqyYkWS94FLXvaDwqsLuW05OjklMTDRVqlQxRYsWNUFBQaZdu3bW+dmzZ5vQ0FDj7u5uIiIizMqVK42Li4vV742+WHHfvn2mefPmxt3d3YSEhJjExEQTGxtr99soTpw4YZ5++mkTEBBg3NzcTJ06dcx3332Xbyzy6//6F0Qa88dLKmvUqGHc3NxMrVq1zKJFi4wkM2fOHKvOzp07TUxMjPHy8jKhoaFm7ty5eb4gMq84X1+eV71r770xJtdnwZjcLwBduXKlCQ8PN+7u7qZKlSpmzpw5dmO6kXUKALi72Iy5yT2CAAD8SXr27Gn99PV2jRgxQjNmzNDu3bsdMLI/x704Ztxdrj6ysmXLFtWqVcvZwwEA/A9xdfYAAACQ/niGfP78+WrSpIlcXFz09ddf69NPP9V7773n7KEB94wPPvhAtWvXVnBwsDIyMjRo0CDVq1ePRAMA4E9HsgEAcFdwcXHR7NmzNWzYMOtFih988IGeeuopZw8NuGccOHBAY8aM0W+//abSpUurefPm+uc//+nsYQEA/gfxGAUAAAAAAHCoQnc2vP/++0pNTVXx4sU1btw4SdL06dO1ceNGubq6qlSpUkpISLB+F/P8+fO1fPlyFSlSRD169FBERIQkKS0tTVOnTlVOTo6aNWumtm3b3sFpAQAAAAAAZyn0V1/GxcVpyJAhdmXh4eEaN26c/vWvf+m+++7T/PnzJUkHDx5UcnKy3n77bb366qv6+OOPlZOTo5ycHH388ccaMmSIxo8frx9//FEHDx68MzMCAAAAAABOVWiyISwsTD4+PnZltWvXlouLiySpSpUqys7OliSlpKSoYcOGKlq0qIKCglS6dGnt3r1bu3fvVunSpVWqVCm5urqqYcOGSklJuQPTAQAAAAAAznbbL4hcvny5GjZsKEnKzs5W5cqVrXP+/v5WIqJkyZJWecmSJbVr164bav/QoUO3O8S/nICAAB09etTZw/ifRfydi/g7F/F3Pu6BcxF/5yL+zkX8nYv4Oxfxz1twcHC+524r2TBv3jy5uLjowQcfvJ1m7CQlJSkpKUmSNHbsWAUEBDis7b8KV1dX4uJExN+5iL9zEX/n4x44F/F3LuLvXMTfuYi/cxH/m3fLyYaVK1dq48aNeu2112Sz2ST9sZPh2LFjVp3s7Gz5+/tLkl35sWPHrPLrxcfHKz4+3jome5QbWTXnIv7ORfydi/g7H/fAuYi/cxF/5yL+zkX8nYv4562gnQ2FvrMhL2lpaVq4cKEGDx4sd3d3qzwqKkrJycm6dOmSDh8+rKysLIWGhqpSpUrKysrS4cOHdfnyZSUnJysqKupWugYAAAAAAHe5Qnc2JCYmKiMjQ6dOndIzzzyj9u3ba/78+bp8+bLefPNNSVLlypX19NNPq0yZMmrQoIGee+45FSlSRL169VKRIn/kM3r27KlRo0YpJydHTZo0UZkyZe7szAAAAAAAgFMUmmwYOHBgrrKmTZvmW/+xxx7TY489lqv8gQce0AMPPHCTwwMAAAAAAPeaW3qMAgAAAAAAID8kGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4lKuzB/BX1GXk5Tvcw693tPUZQ1kWAAAAAIBbx84GAAAAAADgUCQbAAAAAACAQ5FsAAAAAAAADkWyAQAAAAAAOBTJBgAAAAAA4FAkGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4FMkGAAAAAADgUCQbAAAAAACAQ5FsAAAAAAAADkWyAQAAAAAAOBTJBgAAAAAA4FAkGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4FMkGAAAAAADgUCQbAAAAAACAQ5FsAAAAAAAADkWyAQAAAAAAOBTJBgAAAAAA4FAkGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4lGthFd5//32lpqaqePHiGjdunCTp9OnTGj9+vI4cOaLAwEANGjRIPj4+MsZo6tSp2rRpk9zd3ZWQkKCKFStKklauXKl58+ZJkh577DHFxcXduVkBAAAAAACnKXRnQ1xcnIYMGWJXtmDBAtWqVUvvvvuuatWqpQULFkiSNm3apF9//VXvvvuunn76aU2ePFnSH8mJOXPmaPTo0Ro9erTmzJmj06dP34HpAAAAAAAAZys02RAWFiYfHx+7spSUFMXGxkqSYmNjlZKSIknasGGDYmJiZLPZVKVKFZ05c0bHjx9XWlqawsPD5ePjIx8fH4WHhystLe0OTAcAAAAAADjbLb2z4cSJEypRooQkyc/PTydOnJAkZWdnKyAgwKpXsmRJZWdnKzs7WyVLlrTK/f39lZ2dfTvjBgAAAAAAd6lC39lQGJvNJpvN5oixSJKSkpKUlJQkSRo7dqxd8uLe8auzB3Bb7s2Y/3lcXV2JkRMRf+ci/s7HPXAu4u9cxN+5iL9zEX/nIv4375aSDcWLF9fx48dVokQJHT9+XMWKFZP0x46Fo0ePWvWOHTsmf39/+fv7KyMjwyrPzs5WWFhYnm3Hx8crPj7eOr62Pfw5iHnBAgICiJETEX/nIv7Oxz1wLuLvXMTfuYi/cxF/5yL+eQsODs733C09RhEVFaVVq1ZJklatWqW6deta5atXr5YxRpmZmfLy8lKJEiUUERGhzZs36/Tp0zp9+rQ2b96siIiIW+kaAAAAAADc5Qrd2ZCYmKiMjAydOnVKzzzzjNq3b6+2bdtq/PjxWr58ufWrLyUpMjJSqamp6t+/v9zc3JSQkCBJ8vHx0d///ne98sorkqR27drleukkAAAAAAD4ayg02TBw4MA8y1977bVcZTabTb17986zftOmTdW0adObHB4AAAAAALjX3NJjFAAAAAAAAPkh2QAAAAAAAByKZAMAAAAAAHAokg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcimQDAAAAAABwKJINAAAAAADAoUg2AAAAAAAAhyLZAAAAAAAAHIpkAwAAAAAAcCiSDQAAAAAAwKFINgAAAAAAAIci2QAAAAAAAByKZAMAAAAAAHAokg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcimQDAAAAAABwKJINAAAAAADAoUg2AAAAAAAAhyLZAAAAAAAAHIpkAwAAAAAAcCiSDQAAAAAAwKFINgAAAAAAAIci2QAAAAAAAByKZAMAAAAAAHAokg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcimQDAAAAAABwKJINAAAAAADAoUg2AAAAAAAAhyLZAAAAAAAAHIpkAwAAAAAAcCiSDQAAAAAAwKFINgAAAAAAAIci2QAAAAAAAByKZAMAAAAAAHAokg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcytXZAwAcrcvIy3e4h1/vaOszhvKxBAAAAHBvY2cDAAAAAABwKJINAAAAAADAoW5rv/Y333yj5cuXy2azqUyZMkpISNDvv/+uxMREnTp1ShUrVlS/fv3k6uqqS5cu6b333tPevXvl6+urgQMHKigoyFHzAAAAAAAAd4lb3tmQnZ2txYsXa+zYsRo3bpxycnKUnJysGTNmqHXr1powYYK8vb21fPlySdLy5cvl7e2tCRMmqHXr1vrss88cNgkAAAAAAHD3uK3HKHJycnTx4kVduXJFFy9elJ+fn9LT01W/fn1JUlxcnFJSUiRJGzZsUFxcnCSpfv362rZtm4wxtzd6AAAAAABw17nlxyj8/f31yCOP6Nlnn5Wbm5tq166tihUrysvLSy4uLlad7OxsSX/shChZsqQkycXFRV5eXjp16pSKFSvmgGkAAAAAAIC7xS0nG06fPq2UlBRNnDhRXl5eevvtt5WWlnbbA0pKSlJSUpIkaezYsQoICLjtNv98d/ZXI95p92bMr0X8/8pcXV2JkRMRf+fjHjgX8Xcu4u9cxN+5iL9zEf+bd8vJhq1btyooKMjamVCvXj3t3LlTZ8+e1ZUrV+Ti4qLs7Gz5+/tL+mOXw7Fjx1SyZElduXJFZ8+ela+vb6524+PjFR8fbx0fPXr0VoeIW0TMnYv4FywgIIAYORHxdz7ugXMRf+ci/s5F/J2L+DsX8c9bcHBwvudu+Z0NAQEB2rVrly5cuCBjjLZu3ar7779fNWrU0Nq1ayVJK1euVFRUlCSpTp06WrlypSRp7dq1qlGjhmw22612DwAAAAAA7lK3vLOhcuXKql+/vgYPHiwXFxeVL19e8fHxeuCBB5SYmKgvvvhCFSpUUNOmTSVJTZs21Xvvvad+/frJx8dHAwcOdNgkAAAAAADA3eOWkw2S1L59e7Vv396urFSpUhozZkyuum5ubnruuedupzsAAAAAAHAPuK1ffQkAAAAAAHA9kg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcimQDAAAAAABwKJINAAAAAADAoUg2AAAAAAAAhyLZAAAAAAAAHIpkAwAAAAAAcCiSDQAAAAAAwKFINgAAAAAAAIci2QAAAAAAAByKZAMAAAAAAHAokg0AAAAAAMChSDYAAAAAAACHItkAAAAAAAAcimQDAAAAAABwKJINAAAAAADAoVydPQAAfy1dRl6+wz38ekdbnzGUfxYBAACA28XOBgAAAAAA4FAkGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADgUyQYAAAAAAOBQJBsAAAAAAIBDkWwAAAAAAAAORbIBAAAAAAA4FMkGAAAAAADgUCQbAAAAAACAQ5FsAAAAAAAADkWyAQAAAAAAOBTJBgAAAAAA4FAkGwAAAAAAgEORbAAAAAAAAA5FsgEAAAAAADiUq7MHAABwnC4jL9/hHn69w+1LM4bynyYAAIB7HTsbAAAAAACAQ5FsAAAAAAAADkWyAQAA/H/t3W2QlfV9N/DvcdcFAVnYBTSsWrtCGkUiVqi0MUphm1q1E1+kdpLgDGNSqnQk2k4qzWSUadUQDV2DxaE2GZyJ7fTpxXbGsXc6KyPMrWMKstTHokbrE6HI7rrhUdzdc79g3Btc8yB7sRfLfj7vzv7Pda7f+Z3/XBfny/+6DgBAoYQNAAAAQKGEDQAAAEChhA0AAABAoYQNAAAAQKGEDQAAAEChhA0AAABAoWqHsvG+ffuybt26vPnmm6lUKrnpppsyffr0tLa25p133snUqVNz6623ZsKECalWq1m/fn06OjoyZsyYLFu2LM3NzUW9DwAAAOAEMaSVDevXr8+cOXNy33335d57701TU1Pa2toye/bsrFmzJrNnz05bW1uSpKOjIzt37syaNWuydOnSfO973yvkDQAAAAAnlmMOG/bv358XX3wxCxcuTJLU1tZm/Pjx2bx5c6644ookyRVXXJHNmzcnSbZs2ZLLL788lUoln/zkJ7Nv3750d3cX8BYAAACAE8kxX0axa9euTJw4MQ888EBef/31NDc3Z8mSJenp6cnkyZOTJJMmTUpPT0+SpKurK1OmTBnYvrGxMV1dXQPPBQAAAE4Oxxw29PX15bXXXssNN9yQmTNnZv369QOXTHygUqmkUql8rNdtb29Pe3t7kmTVqlVHBRQjx86yCxiSkdnzI+l/R8DEgQAAFdZJREFUufS/XCO7/8nJ8BkcX7W1tXpUIv0vl/6XS//Lpf/l0v+P75jDhsbGxjQ2NmbmzJlJkvnz56etrS319fXp7u7O5MmT093dnYkTJyZJGhoasnv37oHtOzs709DQMOh1W1pa0tLSMvD4yG0YHnpeLv0vl/6Xz2fw802ZMkWPSqT/5dL/cul/ufS/XPr/0aZPn/4zx475ng2TJk1KY2NjduzYkSR59tlnc9ZZZ2Xu3LnZuHFjkmTjxo2ZN29ekmTu3LnZtGlTqtVqXnrppYwbN84lFAAAAHASGtJPX95www1Zs2ZNent7M23atCxbtizVajWtra3ZsGHDwE9fJsnFF1+crVu3Zvny5amrq8uyZcsKeQMAAADAiWVIYcO5556bVatWDfr77bffPuhvlUolX/3qV4eyOwAAAGAEOObLKAAAAAA+irABAAAAKJSwAQAAACiUsAEAAAAolLABAAAAKNSQfo0CADja4jt7j/Medh7XV3/4m/5pAAAMnZUNAAAAQKGEDQAAAEChhA0AAABAoYQNAAAAQKGEDQAAAEChhA0AAABAoYQNAAAAQKGEDQAAAEChhA0AAABAoYQNAAAAQKFqyy4AAICTw+I7e4/zHnYe11d/+Jv+aQxQFCsbAAAAgEKJbwGAk4b/WQeAE4OVDQAAAEChhA0AAABAoYQNAAAAQKFcGAgAACcB9ywBTiRWNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIWqLbsAAAAAGIrFd/Ye5z3sPK6v/vA3T76v5lY2AAAAAIUSNgAAAACFOvnWagAAAAwzy/jhaFY2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhaod6gv09/dnxYoVaWhoyIoVK7Jr167cd9992bNnT5qbm3PzzTentrY277//fv7mb/4mr776ak4//fTccsstmTZtWhHvAQAAADiBDHllw6OPPpqmpqaBxw8//HCuvvrq3H///Rk/fnw2bNiQJNmwYUPGjx+f+++/P1dffXX+/u//fqi7BgAAAE5AQwobOjs7s3Xr1ixatChJUq1W8/zzz2f+/PlJkgULFmTz5s1Jki1btmTBggVJkvnz5+e5555LtVodyu4BAACAE9CQwoaHHnooixcvTqVSSZLs2bMn48aNS01NTZKkoaEhXV1dSZKurq40NjYmSWpqajJu3Ljs2bNnKLsHAAAATkDHfM+Gp59+OvX19Wlubs7zzz9fWEHt7e1pb29PkqxatSpTpkwp7LWHz86yCxiSkdnzI+l/ufS/XCO7/4nPoGz6Xy79L5f+l0v/y6X/5Rr5/R/smMOG7du3Z8uWLeno6MihQ4dy4MCBPPTQQ9m/f3/6+vpSU1OTrq6uNDQ0JDm8yqGzszONjY3p6+vL/v37c/rppw963ZaWlrS0tAw83r1797GWyDHS83Lpf7n0v3w+g3Lpf7n0v1z6Xy79L5f+l2uk9n/69Ok/c+yYL6P40pe+lHXr1mXt2rW55ZZbcuGFF2b58uWZNWtWnnrqqSTJ448/nrlz5yZJLrnkkjz++ONJkqeeeiqzZs0auPwCAAAAOHkM+dcoPuzLX/5yHnnkkdx8883Zu3dvFi5cmCRZuHBh9u7dm5tvvjmPPPJIvvzlLxe9awAAAOAEcMyXURxp1qxZmTVrVpLkjDPOyLe+9a1Bz6mrq8uf/umfFrE7AAAA4ARW+MoGAAAAYHQTNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhao91g13796dtWvX5t13302lUklLS0uuuuqq7N27N62trXnnnXcyderU3HrrrZkwYUKq1WrWr1+fjo6OjBkzJsuWLUtzc/Mv3M8XvtB41ONrrjmQJUv258CBSq6/vmHQ8//gD/bnD//wQLq6TsnSpZMHjV9//b58/vMH8/bbp+RrXxs8vnTp3nzuc+/llVdqsmLFpEHjy5fvyeWXH8pzz9Vm5cr6QeO33fbTJL3p2VmXV380ePsZn+nO6VPeT9dbY/L604O3/7XLuzJucm92/89pefO/Th80fv6izoyd0Jf/fWVcdjw/YdD4rM/tTt1p/fnJf4/Pzu3jB41/+qp3UnNqNW8/NyG7fjxu0PjFn9+VJFm3bnza28ceNTZ2bDUPP9yVJGltnZAnnhhz1Pjkyf35u7/rTpJ861un5+mn644a/8Qn+nL//e8mSW6/fWJeeOHUo8abm3tzzz09SZI///P6vPrq0dPzggvez1/+5U+TJDffPCk/+UnNUeOXXHIof/EXe5Ikz/2fKXn/vaOztMlNB3Pu3MPb/9cjU9PfVzlqvPFXDuScOYe37/i3afmwaeftT9OFe9P3fiXPPDp10PiZv7Yvn/jUvhw6cEqe/48pg8anz9qbM2bsz8G9NXnxscZB42dftCdTzj0wpLk3b9772bz51Hz72xMHja9c2ZMLL+zNpk11WbNm8NxaterdzJjRl//4jzF58MHBc+u73+1OU1N//u3fxuYHPxg8tx58sDsNDUOfe29sOz2dr5921NgpNdVcdM07SZL/2TIx3W8fPTdPHdOfC6/cnST58VP1+en/Hj03x4zvywUtnUmSl//vpOztPHpunlbfm08tODy3hzr3/uiPJqe7++i595nPvJdbb92bJFm8uCEHDx4991paDubGG/clGXzMSz7ece+j5u4vO/f2d9dm+6bBr/8rl/Sk4az3smf3qXnlicHHzeZL3039mYcKO+59YdvR/fll594//dNp+Zd/GTy3fvCDrpx2WjUPPTQujzxy2qDxf/3Xw3OjqOPei69XB8Y/ztz778cbcqDn6Lk3ofFQZl52+Lj5Qntj3tt39NybeMZ7OW/+4eNmUce9I/s/Es65Hz7uHdn/kXLOPfK490H/R9I598jj3gf9Hynn3A8f9z7o/0g55374uPdB/0fKOffDx70vbKuMqHPuh497L75eHXHn3CN9YVtlxJ1zj/Tq7t4Rd8490kNnHRxx59wkefLJQU8dcMwrG2pqanL99dentbU1d911V374wx/mrbfeSltbW2bPnp01a9Zk9uzZaWtrS5J0dHRk586dWbNmTZYuXZrvfe97x7prAAAA4ARWqVar1V/8tF/snnvuyZVXXpnvf//7WblyZSZPnpzu7u6sXLky3/3ud/Pggw/mggsuyGWXXZYk+drXvjbwvJ9nx44dRZQ3rBbf2Vt2CUPy8DePecHLCUH/y6X/5Rrp/U98BmXT/3Lpf7n0v1z6Xy79L9dI7f/06dN/5lgh92zYtWtXXnvttcyYMSM9PT0DAcKkSZPS03N4qUlXV1emTPn/y9waGxvT1dVVxO4BAACAE8iQ45ODBw9m9erVWbJkScaNO/q6nUqlkkql8jO2/Gjt7e1pb29PkqxateqogGLk2Fl2AUMyMnt+JP0vl/6Xa2T3P/EZlE3/y6X/5dL/cul/ufS/XCO//4MNKWzo7e3N6tWr89nPfjaXXnppkqS+vj7d3d0Dl1FMnHj45hENDQ3ZvXv3wLadnZ1paBh844uWlpa0tLQMPD5yG4aHnpdL/8ul/+XzGZRL/8ul/+XS/3Lpf7n0v1wjtf/H5TKKarWadevWpampKddcc83A3+fOnZuNGzcmSTZu3Jh58+YN/H3Tpk2pVqt56aWXMm7cuF94vwYAAABg5DnmlQ3bt2/Ppk2bcs455+TrX/96kuSLX/xirr322rS2tmbDhg0DP32ZJBdffHG2bt2a5cuXp66uLsuWLSvmHQAAAAAnlGMOGz71qU/ln//5nz9y7Pbbbx/0t0qlkq9+9avHujsAAABghCjk1ygAAAAAPiBsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKJWwAAAAACiVsAAAAAAolbAAAAAAKVTvcO9y2bVvWr1+f/v7+LFq0KNdee+1wlwAAAAAcR8O6sqG/vz/f//73841vfCOtra154okn8tZbbw1nCQAAAMBxNqxhwyuvvJIzzzwzZ5xxRmpra/Nbv/Vb2bx583CWAAAAABxnwxo2dHV1pbGxceBxY2Njurq6hrMEAAAA4DirVKvV6nDt7Kmnnsq2bdty4403Jkk2bdqUl19+OV/5ylcGntPe3p729vYkyapVq4arNAAAAKAgw7qyoaGhIZ2dnQOPOzs709DQcNRzWlpasmrVKkHDz7FixYqySxjV9L9c+l8u/S+fz6Bc+l8u/S+X/pdL/8ul/x/fsIYN5513Xn7yk59k165d6e3tzZNPPpm5c+cOZwkAAADAcTasP31ZU1OTG264IXfddVf6+/vz27/92zn77LOHswQAAADgOKtZuXLlyuHc4Sc+8Yn83u/9Xq666qqcf/75w7nrk0pzc3PZJYxq+l8u/S+X/pfPZ1Au/S+X/pdL/8ul/+XS/49nWG8QCQAAAJz8hvWeDQAAAMDJb1jv2cDQPPDAA9m6dWvq6+uzevXqsssZdXbv3p21a9fm3XffTaVSSUtLS6666qqyyxo1Dh06lDvuuCO9vb3p6+vL/Pnzc91115Vd1qjT39+fFStWpKGhwV2Zh9mf/MmfZOzYsTnllFNSU1PjV5uG2b59+7Ju3bq8+eabqVQquemmm/LJT36y7LJGhR07dqS1tXXg8a5du3Ldddfl6quvLrGq0eWRRx7Jhg0bUqlUcvbZZ2fZsmWpq6sru6xR49FHH81jjz2WarWaRYsWmfvD4KO+d+3duzetra155513MnXq1Nx6662ZMGFCyZWe2IQNI8iCBQty5ZVXZu3atWWXMirV1NTk+uuvT3Nzcw4cOJAVK1bk05/+dM4666yySxsVTj311Nxxxx0ZO3Zsent7c/vtt2fOnDn+sT/MHn300TQ1NeXAgQNllzIq3XHHHZk4cWLZZYxK69evz5w5c/Jnf/Zn6e3tzXvvvVd2SaPG9OnTc++99yY5HHj+8R//cX7jN36j5KpGj66urvz7v/97WltbU1dXl7/+67/Ok08+mQULFpRd2qjwxhtv5LHHHsvdd9+d2tra3H333bnkkkty5plnll3aSe2jvne1tbVl9uzZufbaa9PW1pa2trYsXry4xCpPfC6jGEEuuOAC6VmJJk+ePHBTmNNOOy1NTU3p6uoquarRo1KpZOzYsUmSvr6+9PX1pVKplFzV6NLZ2ZmtW7dm0aJFZZcCw2r//v158cUXs3DhwiRJbW1txo8fX3JVo9Ozzz6bM888M1OnTi27lFGlv78/hw4dSl9fXw4dOpTJkyeXXdKo8fbbb2fGjBkZM2ZMampqcv755+dHP/pR2WWd9D7qe9fmzZtzxRVXJEmuuOKKbN68uYzSRhQrG+AY7Nq1K6+99lpmzJhRdimjSn9/f2677bbs3Lkzv/u7v5uZM2eWXdKo8tBDD2Xx4sVWNZTorrvuSpL8zu/8TlpaWkquZvTYtWtXJk6cmAceeCCvv/56mpubs2TJkoEAlOHzxBNP5DOf+UzZZYwqDQ0N+f3f//3cdNNNqaury0UXXZSLLrqo7LJGjbPPPjv/+I//mD179qSuri4dHR0577zzyi5rVOrp6RkI2iZNmpSenp6SKzrxWdkAH9PBgwezevXqLFmyJOPGjSu7nFHllFNOyb333pt169blxz/+cd54442ySxo1nn766dTX1/vJpxL91V/9Vb797W/nG9/4Rn74wx/mhRdeKLukUaOvry+vvfZaPve5z+Wee+7JmDFj0tbWVnZZo05vb2+efvrpzJ8/v+xSRpW9e/dm8+bNWbt2bf72b/82Bw8ezKZNm8oua9Q466yz8vnPfz533nln7r777px77rk55RRf4cpWqVSssP0lWNkAH0Nvb29Wr16dz372s7n00kvLLmfUGj9+fGbNmpVt27blnHPOKbucUWH79u3ZsmVLOjo6cujQoRw4cCBr1qzJ8uXLyy5t1GhoaEiS1NfXZ968eXnllVdywQUXlFzV6NDY2JjGxsaB1VTz588XNpSgo6Mjv/qrv5pJkyaVXcqo8uyzz2batGkD94u59NJL89JLL+Xyyy8vubLRY+HChQOXcf3DP/xDGhsbS65odKqvr093d3cmT56c7u5u91D6JYjF4JdUrVazbt26NDU15Zprrim7nFHnpz/9afbt25fk8C9TPPPMM2lqaiq5qtHjS1/6UtatW5e1a9fmlltuyYUXXihoGEYHDx4cuHzl4MGDeeaZZwRtw2jSpElpbGzMjh07khz+8uXmwMPPJRTlmDJlSl5++eW89957qVarefbZZ51/h9kHy/V3796d//zP/8xll11WckWj09y5c7Nx48YkycaNGzNv3rySKzrxWdkwgtx333154YUXsmfPntx444257rrrBlJOjr/t27dn06ZNOeecc/L1r389SfLFL34xv/7rv15yZaNDd3d31q5dm/7+/lSr1fzmb/5mLrnkkrLLgmHR09OT73znO0kOL+m/7LLLMmfOnJKrGl1uuOGGrFmzJr29vZk2bVqWLVtWdkmjygch29KlS8suZdSZOXNm5s+fn9tuuy01NTU599xz3TNmmK1evTp79uxJbW1tvvKVr7hB7TD4qO9d1157bVpbW7Nhw4aBn77k56tUq9Vq2UUAAAAAJw+XUQAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhRI2AAAAAIUSNgAAAACFEjYAAAAAhfp/LNcwIvro8jwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8sVmb44l9gy"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jksGTpRxDGd8"
      },
      "source": [
        "\n",
        "### **OUTRAS PERGUNTAS (FAÇA COMO EXERCÍCIO)**\n",
        "\n",
        "Considere o contexto do projeto do álbum de figurinhas e responda as perguntas adicionais: \n",
        "\n",
        "1. Quantas vezes saiu a figurinha mais repetida, em média?\n",
        "2. Em média, quantas figurinhas não se repetem ao completar o álbum?\n",
        "3. Faça um gráfico da porcentagem completada esperada em função do número de pacotes comprados. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCY_R2exFSDX"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rBE-DdbJ3Am"
      },
      "source": [
        "## **Exercícios Complementares: Python para Data Science**  <a name=\"exer\"></a>\n",
        "Os exercícios abaixo foram pensados para que iniciantes desenvolvam manejo e intimidade programando em python, utilizando os recursos fundamentais da linguagem.\n",
        "\n",
        "Os dois primeiros exercícios são mais simples. O exercício 3 já é um pouco mais elaborado e pode ser resolvido de várias formas. E o último exercício é apenas um complemento do projeto desenvolvido em aula sobre o álbum de figurinhas. Foram feitas mais perguntas para serem respondidas com o experimento. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1X1E_zEge7K"
      },
      "source": [
        "### **Exercício 1** \n",
        "Considere um balde cuja base possui raio  $r_1$ e altura igual ao diâmetro da base. Considere também uma esfera de raio $r_2$ cheia de água. Faça um programa que verifique se o volume da esfera cabe no balde, dados os valores de $r_1$ e $r_2$. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRfHvmZ548aq"
      },
      "source": [
        "\n",
        "### **Exercício 2** \n",
        "Crie uma função que simule o jogo do *jokempô*, isto é, dada a entrada de dois jogadores, retorne a indicação de qual deles venceu. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XW7xW90KucKA"
      },
      "source": [
        "### **Exercício 3**  \n",
        "\n",
        "Faça um programa que simule uma **slot machine**. Uma slot machine é uma máquina muito comum em cassinos. A pessoa puxa uma alavanca e aparecem na tela 3 símbolos aleatoriamente, de uma lista com diversos deles. Se os símbolos forem iguais, então a pessoa ganha. A pessoa entrar com um tanto escolhido de fichas, e joga até que acabe. Quando o programa terminar, uma mensagem resumindo os totais que ela ganhou deve ser exibida."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSJC-C50lrdi"
      },
      "source": [
        " ## **Links Úteis** <a name=\"links\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvCE4Gnglshy"
      },
      "source": [
        "1. [Documentação do Python](https://docs.python.org/3/)\n",
        "2. [Download do Anaconda](https://anaconda.org/)\n",
        "3. [Curso do Gustavo Guanabara no YouTube (Canal Curso em Vídeo)](https://www.youtube.com/watch?v=S9uPNppGsGo&list=PLvE-ZAFRgX8hnECDn1v9HNTI71veL3oW0)\n",
        "4. [Curso de Python gratuito do Kaggle (inglês)](https://www.kaggle.com/learn/python)\n",
        "5. [Conceitos de Python em 40min por Derek Banas (inglês)](https://www.youtube.com/watch?v=N4mEzFDjqtA) \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UE-KpVQlpZYZ"
      },
      "source": [
        "## **Anexo** <a name=\"anex\"></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Qt56rwNP2rW"
      },
      "source": [
        "\n",
        "### **The Zen of Python, por Tim Peters**\n",
        "\n",
        "É um conjunto de regras pela qual o Python é idealizado.\n",
        "\n",
        "1. Bonito é melhor que feio.\n",
        "2. Explícito é melhor que implícito.\n",
        "3.  Simples é melhor que complexo.\n",
        "4. Complexo é melhor que complicado.\n",
        "5. Plano é melhor que aglomerado.\n",
        "6. Esparso é melhor que denso.\n",
        "7. Legibilidade faz diferença.\n",
        "8. Casos especiais não são especiais o bastante para quebrar as regras.\n",
        "9. Embora a praticidade vença a pureza.\n",
        "10. Erros nunca devem passar silenciosamente.\n",
        "11. A menos que sejam explicitamente silenciados.\n",
        "12. Diante da ambigüidade, recuse a tentação de adivinhar.\n",
        "13. Deve haver um -- e preferencialmente só um -- modo óbvio para fazer algo.\n",
        "14. Embora esse modo possa não ser óbvio à primeira vista a menos que você seja holandês.\n",
        "15. Agora é melhor que nunca.\n",
        "16. Embora nunca freqüentemente seja melhor que *exatamente* agora.\n",
        "17. Se a implementação é difícil de explicar, é uma má idéia.\n",
        "18. Se a implementação é fácil de explicar, pode ser uma boa idéia.\n",
        "19. Namespaces são uma grande idéia -- vamos fazer mais dessas! "
      ]
    }
  ]
}